ST1504 DEEP LEARNING CA2 PART C¶

NAME: EDWARD TAN YUAN CHONG

CLASS: DAAA/FT/2B/04

ADM NO.: 2214407

IMPORT MODULES¶

In [1]:
# Standard libraries
import os
import time
import warnings
import re

# Data handling
import numpy as np
import pandas as pd
from PIL import Image

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib

# PyTorch basics
import torch
import torch.nn as nn
import torch.optim as optim

# PyTorch helpers for data loading and transforms
from torch.utils.data import DataLoader, Dataset
import torchvision.transforms as transforms
import torchvision.utils as vutils

# Metrics and evaluation
from torchmetrics.image.fid import FrechetInceptionDistance
from torchmetrics.image import InceptionScore
from torchsummary import summary

# Progress bar
from tqdm import tqdm

# Ignore warnings
warnings.filterwarnings('ignore')

PLOTTING CUSTOMIZATIONS¶

In [2]:
# Change theme of charts
sns.set_theme(style='darkgrid')
# Change font of charts
sns.set(font='Century Gothic')
# Variable for color palettes
color_palette = sns.color_palette('muted')
# Increase embed limit for animation
matplotlib.rcParams['animation.embed_limit'] = 100

IMPORT DATA¶

We only load the first 2000 images of the dataset.

Dataset can be found on Kaggle: https://www.kaggle.com/datasets/spandan2/cats-faces-64x64-for-generative-models/data

In [44]:
class CatImagesDataset(Dataset):
    def __init__(self, directory, transform=None, limit=2000):
        self.directory = directory
        self.transform = transform
        self.image_paths = [os.path.join(directory, fname) for fname in os.listdir(directory) if fname.endswith('.jpg')][:limit]

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        img_path = self.image_paths[idx]
        with open(img_path, 'rb') as f:
            img = Image.open(f)
            img = img.convert('RGB')
            if self.transform:
                img = self.transform(img)
        return img
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
cat_dataset = CatImagesDataset('./cat_ca2/cats', transform=transform)

batch_size = 64
cat_dataloader = DataLoader(cat_dataset, batch_size=batch_size, shuffle=True)

images = []
for batch in cat_dataloader:
    images.append(batch)

images = torch.cat(images)

CONSTANTS¶

In [47]:
IMAGE_SIZE = images[0].size()[1]
CRITIC_ITERATIONS = 5
WEIGHT_CLIP = 0.01
LR = 2e-4
EPOCHS = 200
nz = 100
ndf = 128
ngf = 128
nc = 3
ngpu = 1
real_label = 1
fake_label = 0
color_palette = sns.color_palette('muted')
device = torch.device('cuda' if (torch.cuda.is_available() and ngpu > 0) else 'cpu')
print('Device used:', device)
Device used: cuda

VISUALIZE IMAGES¶

In [48]:
def normalize_images(images):
    normalized_images = [(image + 1) / 2 for image in images]
    return normalized_images
In [49]:
def plot_images(images, rows=2, cols=8):
    fig, axs = plt.subplots(rows, cols, figsize=(cols*3, rows*3))
    axs = axs.ravel()
    for i in range(min(len(images), rows * cols)):
        axs[i].imshow(images[i].permute(1, 2, 0)) 
        axs[i].axis('off')
    plt.show()
In [50]:
plot_images(normalize_images(images))
No description has been provided for this image

HELPER FUNCTIONS¶

In [8]:
# Monitor Class for GANs
class GANMonitor:
    def __init__(self, patience=5):
        self.G_losses = []
        self.D_losses = []
        self.fid_scores = []
        self.is_scores = []
        self.best_fid_iter = 0
        self.kl_divergence = None
        self.best_fid = float('inf')
        self.improve = True
 
    def plot_loss(self):
        # Plotting
        plt.figure(figsize=(10,6))
        sns.lineplot(self.G_losses, label='Generator Loss', color=color_palette[2])
        sns.lineplot(self.D_losses, label='Discriminator Loss', color=color_palette[3])
        plt.title('Generator vs Discriminator Loss')
        plt.xlabel('Iterations')
        plt.ylabel('Loss')
        plt.legend()
        plt.show()
    
    @staticmethod
    def transform(tensor):
        tensor = (tensor + 1) / 2  # Rescale to [0, 1]
        return tensor

    def calc_fid(self,real,fake,i):
        fid = FrechetInceptionDistance(feature=2048, normalize=True, reset_real_features=True).to('cuda')
        real_images_norm = self.transform(real.to('cuda'))
        fid.update(real_images_norm, real=True)
        fake_images_norm = self.transform(fake.to('cuda'))
        fid.update(fake_images_norm, real=False)
        fid_score = fid.compute()
        if fid_score < self.best_fid:
            self.best_fid = fid_score
            self.best_fid_iter = i
            self.improve = True
        else:
            self.improve = False
        return fid_score
    
    def calc_is(self,imgs):
        inception = InceptionScore(normalize=True)
        norm_imgs = self.transform(imgs.to('cpu'))
        scaled_imgs = (norm_imgs * 255).clamp(0, 255).to(torch.uint8)
        inception.update(scaled_imgs)
        return inception.compute()
    
    def plot_scores(self):
        # Convert tensors to lists using list comprehension
        fid_scores = [score.item() for score in self.fid_scores]
        inception_scores, standard_deviations = zip(*[(score.item(), std.item()) for score, std in self.is_scores])

        # Print best results
        min_fid = min(fid_scores)
        max_is = max(inception_scores)
        
        # Print results
        print(f"Minimum FID Score of {min_fid} obtained at iteration of {self.best_fid_iter}.")
        print(f"Maximum IS Score of {max_is}.")

        # Create a figure with three subplots
        fig, axes = plt.subplots(1, 3, figsize=(16, 5))

        # Define common properties for line plots
        plot_props = {'marker': 'o', 'linestyle': '-', 'x': range(len(fid_scores))}

        # FID Scores Line Plot
        sns.lineplot(y=fid_scores, color=color_palette[0], ax=axes[0], **plot_props)
        axes[0].set(title="FID Scores", xlabel="Index", ylabel="FID Score")

        # Inception Scores Line Plot
        sns.lineplot(y=inception_scores, label='Inception Score', color=color_palette[1], ax=axes[1], **plot_props)
        axes[1].set(title='Inception Scores', xlabel='Index', ylabel='Inception Score')
        axes[1].legend()
        axes[1].grid(True)

        # Standard Deviations Line Plot
        sns.lineplot(y=standard_deviations, label='Standard Deviation', color=color_palette[2], ax=axes[2], **plot_props)
        axes[2].set(title='Inception Standard Deviations', xlabel='Index', ylabel='Standard Deviation')
        axes[2].legend()
        axes[2].grid(True)

        # Change suptitle
        fig.suptitle(t='Fréchet Inception Distance and Inception Scores')

        # Show the plot
        plt.tight_layout()
        plt.show()

    def save_weights(self,i,netG, critic, filepath):
        if self.improve:
            # Define your desired file path
            model_dir = filepath
            # Check if the directory exists, and create it if it doesn't
            if not os.path.exists(model_dir):
                os.makedirs(model_dir)
            # Save generator and discriminator weights
            torch.save(netG, filepath + f"/generator-{i}.pth")
            torch.save(critic, filepath + f"/critic-{i}.pth")

    def store_print_metrics(self, real, fake,i):
        current_fid_score = self.calc_fid(real=real, fake=fake, i=i)
        current_is_score = self.calc_is(imgs=fake)
        self.fid_scores.append(current_fid_score)
        self.is_scores.append(current_is_score)
        print(f"Current scores at iteration {i} | FID: {current_fid_score} | IS: {current_is_score[0].item()}")


# ============
# |   WGAN   |
# ============

class WGANGenerator(nn.Module):
    def __init__(self):
        super(WGANGenerator, self).__init__()

        self.model = nn.Sequential(
            self._block(nz, ngf * 8, 4, 1, 0),
            self._block(ngf * 8, ngf * 4), 
            self._block(ngf * 4, ngf * 2), 
            self._block(ngf * 2, ngf),
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def _block(self, in_channels, out_channels, kernel_size=4, stride=2, padding=1, batch_norm=True):
        layers = [nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=not batch_norm)]
        if batch_norm:
            layers.append(nn.BatchNorm2d(out_channels))
        layers.append(nn.LeakyReLU(0.2))

        return nn.Sequential(*layers)
        
    def forward(self, z):
        return self.model(z)
    
# WGAN Critic
class WGANCritic(nn.Module):
    def __init__(self):
        super(WGANCritic, self).__init__()

        self.model = nn.Sequential(
            self._block(nc, ndf, batch_norm=False),
            self._block(ndf, ndf * 2),
            self._block(ndf * 2, ndf * 4),
            nn.Conv2d(ndf * 4, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def _block(self, in_channels, out_channels, kernel_size=4, stride=2, padding=1, batch_norm=True):
        layers = [nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=not batch_norm)]
        if batch_norm:
            layers.append(nn.BatchNorm2d(out_channels, momentum=0.9))
        layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)

class WGANTrainer(GANMonitor):
    def __init__(self, critic, netG, dataloader, device, fixed_noise, real_label, fake_label, nz, optimizerC, optimizerG, num_epochs, weight_clip, critic_iterations):
        super().__init__()
        self.critic = critic
        self.netG = netG
        self.dataloader = dataloader
        self.device = device
        self.fixed_noise = fixed_noise
        self.real_label = real_label
        self.fake_label = fake_label
        self.nz = nz
        self.optimizerC = optimizerC
        self.optimizerG = optimizerG
        self.num_epochs = num_epochs
        self.weight_clip = weight_clip
        self.critic_iterations = critic_iterations
        self.file_path = "./wgan_weights"
        self.temp_errC = []
        self.iters = 0
        self.img_list = []

    def train(self):
        print("Starting Training Loop...")
        for epoch in tqdm(range(self.num_epochs), desc="Training Progress"):
            for i, data in enumerate(self.dataloader, 0):
                start_time = time.time()
                # Train discriminator
                errC, errG, real, fake = self.train_step(data)
                # Train generator
                end_time = time.time()
                iteration_time = end_time - start_time
                # At every 50 iterations, record current loss of the Generator
                if i % self.critic_iterations == 0:
                    print('[%d/%d][%d/%d] \tLoss_D: %.4f | Loss_G: %.4f | Iteration Time: %.4f sec'
                % (epoch, self.num_epochs, i, len(self.dataloader),
                    errC.item(), errG.item(), iteration_time))
                self.check_generator_progress(epoch, i, real, fake)

    def train_step(self, data):
        # Train critic for n critic iterations
        for _ in range(self.critic_iterations):
            # Reset gradients for Discriminator
            self.critic.zero_grad()
            # Real images
            real = data.to(self.device)
            # Noise
            noise = torch.randn(data.size(0), self.nz, 1, 1, device=self.device)
            # Fake image batch generated with G
            fake_data = self.netG(noise)
            # Forward pass real batch through C
            real_output = self.critic(real).view(-1)
            # Output of C when given fake/generated data
            fake_output = self.critic(fake_data.detach()).view(-1)
            # Calculate loss for C
            errC = -(torch.mean(real_output) - torch.mean(fake_output))
            # Append to temporary array
            self.temp_errC.append(errC)
            # Backpropagate for C
            errC.backward()
            self.optimizerC.step()
            # Weight clipping for C
            for p in self.critic.parameters():
                p.data.clamp_(-self.weight_clip, self.weight_clip)
            
        # Reset gradients for Generator
        self.netG.zero_grad()
        # Since we just updated D, perform another forward pass of fake_data through D
        fake_output = self.critic(fake_data).view(-1)
        # Calculate error for G
        errG = -torch.mean(fake_output)
        # Store generator loss
        self.G_losses.append(errG.item())
        # Store average critic loss
        self.D_losses.append((sum(self.temp_errC)/len(self.temp_errC)).item())
        # Reset temp errC array
        self.temp_errC = []
        # Backpropagate for G
        errG.backward()
        self.optimizerG.step()
        return errC, errG, real, fake_data
    
    def check_generator_progress(self, epoch, i, real, fake):
        if (self.iters % 500 == 0) or ((epoch == self.num_epochs-1) and (i == len(self.dataloader)-1)):
            with torch.no_grad():
                # Calculate, store and print FID and IS score
                self.store_print_metrics(real=real, fake=fake, i=self.iters)
                # Save weights
                self.save_weights(self.iters,self.netG, self.critic, self.file_path)
                # Generate image and create image grid
                fake = self.netG(self.fixed_noise).detach().cpu()
                generated_image = np.transpose(vutils.make_grid(fake, padding=2, normalize=True), (1,2,0))
                # Plot image grid
                self.img_list.append(generated_image)
                plt.figure(figsize=(20,6))
                plt.axis("off")
                plt.title(f"Generated Images at {self.iters} iterations")
                plt.imshow(generated_image)
                plt.show()
        self.iters += 1

# ==============
# |   WGAN-GP  |
# ==============
    
class WGANGPGenerator(nn.Module):
    def __init__(self, nz=100, ngf=64, nc=3):
        super(WGANGPGenerator, self).__init__()

        self.model = nn.Sequential(
            self._block(nz, ngf * 8, 4, 1, 0),
            self._block(ngf * 8, ngf * 4), 
            self._block(ngf * 4, ngf * 2), 
            self._block(ngf * 2, ngf),
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def _block(self, in_channels, out_channels, kernel_size=4, stride=2, padding=1, batch_norm=True):
        layers = [nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=not batch_norm)]
        if batch_norm:
            layers.append(nn.BatchNorm2d(out_channels))
        layers.append(nn.LeakyReLU(0.2))

        return nn.Sequential(*layers)
    
    def forward(self, z):
        return self.model(z)

    
# WGAN Critic
class WGANGPCritic(nn.Module):
    def __init__(self):
        super(WGANGPCritic, self).__init__()

        self.model = nn.Sequential(
            self._block(nc, ndf, batch_norm=False),
            self._block(ndf, ndf * 2),
            self._block(ndf * 2, ndf * 4),
            nn.Conv2d(ndf * 4, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def _block(self, in_channels, out_channels, kernel_size=4, stride=2, padding=1, batch_norm=True):
        layers = [nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=not batch_norm)]
        if batch_norm:
            layers.append(nn.InstanceNorm2d(out_channels, momentum=0.9))
        layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)
    
class WGANGPTrainer(GANMonitor):
    def __init__(self, critic, netG, dataloader, device, fixed_noise, real_label, fake_label, nz, optimizerC, optimizerG, num_epochs, critic_iterations, lambda_gp):
        super().__init__()
        self.critic = critic
        self.netG = netG
        self.dataloader = dataloader
        self.device = device
        self.fixed_noise = fixed_noise
        self.real_label = real_label
        self.fake_label = fake_label
        self.nz = nz
        self.optimizerC = optimizerC
        self.optimizerG = optimizerG
        self.num_epochs = num_epochs
        self.critic_iterations = critic_iterations
        self.lambda_gp = lambda_gp
        self.file_path = "./wgan-gp_weights"
        self.temp_errC = []
        self.iters = 0
        self.img_list = []

    def train(self):
        print("Starting Training Loop...")
        for epoch in tqdm(range(self.num_epochs), desc="Training Progress"):
            for i, data in enumerate(self.dataloader, 0):
                start_time = time.time()
                # Train discriminator
                errC, errG, real, fake = self.train_step(data)
                # Train generator
                end_time = time.time()
                iteration_time = end_time - start_time
                # At every 50 iterations, record current loss of the Generator
                if i % self.critic_iterations == 0:
                    print('[%d/%d][%d/%d] \tLoss_D: %.4f | Loss_G: %.4f | Iteration Time: %.4f sec'
                % (epoch, self.num_epochs, i, len(self.dataloader),
                    errC.item(), errG.item(), iteration_time))
                self.check_generator_progress(epoch, i, real, fake)

    def train_step(self, data):
        # Train critic for n critic iterations
        for _ in range(self.critic_iterations):
            # Reset gradients for Discriminator
            self.critic.zero_grad()
            # Real images
            real = data.to(self.device)
            # Noise
            noise = torch.randn(data.size(0), self.nz, 1, 1, device=self.device)
            # Fake image batch generated with G
            fake_data = self.netG(noise)
            # Forward pass real batch through C
            real_output = self.critic(real).view(-1)
            # Output of C when given fake/generated data
            fake_output = self.critic(fake_data.detach()).view(-1)
            # Gradient penalty
            gp = self.gradient_penalty(self.critic, real, fake_data, device=self.device)
            # Calculate loss for C
            errC = (-(torch.mean(real_output) - torch.mean(fake_output)) + self.lambda_gp * gp)
            # Append to temporary array
            self.temp_errC.append(errC)
            # Backpropagate for C
            errC.backward(retain_graph=True)
            self.optimizerC.step()
    
        # Reset gradients for Generator
        self.netG.zero_grad()
        # Since we just updated D, perform another forward pass of fake_data through D
        fake_output = self.critic(fake_data).view(-1)
        # Calculate error for G
        errG = -torch.mean(fake_output)
        # Store generator loss
        self.G_losses.append(errG.item())
        # Store average critic loss
        self.D_losses.append((sum(self.temp_errC)/len(self.temp_errC)).item())
        # Reset temp errC array
        self.temp_errC = []
        # Backpropagate for G
        errG.backward()
        self.optimizerG.step()

        return errC, errG, real, fake_data
    
    def check_generator_progress(self, epoch, i, real, fake):
        if (self.iters % 500 == 0) or ((epoch == self.num_epochs-1) and (i == len(self.dataloader)-1)):
            with torch.no_grad():
                # Calculate, store and print FID and IS score
                self.store_print_metrics(real=real, fake=fake, i=self.iters)
                # Save weights
                self.save_weights(self.iters,self.netG, self.critic, self.file_path)
                # Generate image and create image grid
                fake = self.netG(self.fixed_noise).detach().cpu()
                generated_image = np.transpose(vutils.make_grid(fake, padding=2, normalize=True), (1,2,0))
                # Plot image grid
                self.img_list.append(generated_image)
                plt.figure(figsize=(20,6))
                plt.axis("off")
                plt.title(f"Generated Images at {self.iters} iterations")
                plt.imshow(generated_image)
                plt.show()
        self.iters += 1

    @staticmethod
    def gradient_penalty(critic, real, fake, device='cpu'):
        batch_size, C, H, W = real.shape
        epsilon = torch.rand((batch_size, 1, 1, 1)).repeat(1, C, H, W).to(device) # Create interpolated images
        interpolated_images = real * epsilon + fake * (1 - epsilon) # Interpolate real image with fake image

        # Calculate critic score
        mixed_scores = critic(interpolated_images)
        gradient = torch.autograd.grad(
            inputs=interpolated_images,
            outputs=mixed_scores,
            grad_outputs=torch.ones_like(mixed_scores),
            create_graph=True,
            retain_graph=True
        )[0]
        gradient = gradient.view(gradient.shape[0], -1)
        gradient_norm = gradient.norm(2, dim=1)
        gradient_penalty = torch.mean((gradient_norm-1)**2)

        return gradient_penalty

WGAN¶

GENERATOR¶

In [9]:
# Create generator
netG = WGANGenerator().to(device=device)
# Print generator
print(netG)
WGANGenerator(
  (model): Sequential(
    (0): Sequential(
      (0): ConvTranspose2d(100, 1024, kernel_size=(4, 4), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (1): Sequential(
      (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (2): Sequential(
      (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (3): Sequential(
      (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (4): ConvTranspose2d(128, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (5): Tanh()
  )
)

CRITIC¶

In [10]:
# Create Critic
critic = WGANCritic().to(device=device)
# Print critic
print(critic)
WGANCritic(
  (model): Sequential(
    (0): Sequential(
      (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (1): ReLU(inplace=True)
    )
    (1): Sequential(
      (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (2): Sequential(
      (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (3): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (4): Sigmoid()
  )
)

OPTIMIZERS¶

In [11]:
# Optimizers
optimizerC = optim.RMSprop(critic.parameters(), lr=LR, alpha=0.9)
optimizerG = optim.RMSprop(netG.parameters(), lr=LR, alpha=0.9)
# Fixed noise (latent vectors)
fixed_noise = torch.randn(IMAGE_SIZE, nz, 1, 1, device=device)

TRAINING¶

In [12]:
wgan_trainer = WGANTrainer(critic, netG, cat_dataloader, device, fixed_noise, real_label, fake_label, nz, optimizerC, optimizerG, EPOCHS, WEIGHT_CLIP, CRITIC_ITERATIONS)
wgan_trainer.train()
Starting Training Loop...
Training Progress:   0%|          | 0/200 [00:00<?, ?it/s]
[0/200][0/32] 	Loss_D: -0.0061 | Loss_G: -0.4950 | Iteration Time: 0.8203 sec
Current scores at iteration 0 | FID: 399.6596374511719 | IS: 1.0986696481704712
No description has been provided for this image
[0/200][5/32] 	Loss_D: -0.0355 | Loss_G: -0.4774 | Iteration Time: 0.0926 sec
[0/200][10/32] 	Loss_D: -0.0771 | Loss_G: -0.4552 | Iteration Time: 0.0915 sec
[0/200][15/32] 	Loss_D: -0.1251 | Loss_G: -0.4448 | Iteration Time: 0.0910 sec
[0/200][20/32] 	Loss_D: -0.1483 | Loss_G: -0.4428 | Iteration Time: 0.0895 sec
[0/200][25/32] 	Loss_D: -0.1391 | Loss_G: -0.4183 | Iteration Time: 0.0880 sec
Training Progress:   0%|          | 1/200 [00:10<34:12, 10.32s/it]
[0/200][30/32] 	Loss_D: -0.1369 | Loss_G: -0.4088 | Iteration Time: 0.0905 sec
[1/200][0/32] 	Loss_D: -0.1676 | Loss_G: -0.4215 | Iteration Time: 0.0890 sec
[1/200][5/32] 	Loss_D: -0.1389 | Loss_G: -0.4075 | Iteration Time: 0.0890 sec
[1/200][10/32] 	Loss_D: -0.1614 | Loss_G: -0.4085 | Iteration Time: 0.0890 sec
[1/200][15/32] 	Loss_D: -0.1779 | Loss_G: -0.4080 | Iteration Time: 0.0895 sec
[1/200][20/32] 	Loss_D: -0.1684 | Loss_G: -0.4107 | Iteration Time: 0.0905 sec
[1/200][25/32] 	Loss_D: -0.1813 | Loss_G: -0.4132 | Iteration Time: 0.0900 sec
Training Progress:   1%|          | 2/200 [00:14<21:25,  6.49s/it]
[1/200][30/32] 	Loss_D: -0.1916 | Loss_G: -0.4038 | Iteration Time: 0.0890 sec
[2/200][0/32] 	Loss_D: -0.1625 | Loss_G: -0.4080 | Iteration Time: 0.0895 sec
[2/200][5/32] 	Loss_D: -0.1826 | Loss_G: -0.4075 | Iteration Time: 0.0885 sec
[2/200][10/32] 	Loss_D: -0.1810 | Loss_G: -0.4110 | Iteration Time: 0.0915 sec
[2/200][15/32] 	Loss_D: -0.0916 | Loss_G: -0.4696 | Iteration Time: 0.0930 sec
[2/200][20/32] 	Loss_D: -0.1654 | Loss_G: -0.4080 | Iteration Time: 0.0900 sec
[2/200][25/32] 	Loss_D: -0.1647 | Loss_G: -0.4103 | Iteration Time: 0.0895 sec
Training Progress:   2%|▏         | 3/200 [00:17<17:19,  5.28s/it]
[2/200][30/32] 	Loss_D: -0.1698 | Loss_G: -0.4166 | Iteration Time: 0.0857 sec
[3/200][0/32] 	Loss_D: -0.1822 | Loss_G: -0.4110 | Iteration Time: 0.0900 sec
[3/200][5/32] 	Loss_D: -0.1496 | Loss_G: -0.4056 | Iteration Time: 0.0905 sec
[3/200][10/32] 	Loss_D: -0.1800 | Loss_G: -0.4053 | Iteration Time: 0.0895 sec
[3/200][15/32] 	Loss_D: -0.1468 | Loss_G: -0.4034 | Iteration Time: 0.0895 sec
[3/200][20/32] 	Loss_D: -0.1724 | Loss_G: -0.4093 | Iteration Time: 0.0890 sec
[3/200][25/32] 	Loss_D: -0.1794 | Loss_G: -0.4135 | Iteration Time: 0.0910 sec
Training Progress:   2%|▏         | 4/200 [00:21<15:22,  4.70s/it]
[3/200][30/32] 	Loss_D: -0.1670 | Loss_G: -0.4147 | Iteration Time: 0.0905 sec
[4/200][0/32] 	Loss_D: -0.1908 | Loss_G: -0.4041 | Iteration Time: 0.0915 sec
[4/200][5/32] 	Loss_D: -0.1953 | Loss_G: -0.4027 | Iteration Time: 0.0894 sec
[4/200][10/32] 	Loss_D: -0.1886 | Loss_G: -0.4071 | Iteration Time: 0.0910 sec
[4/200][15/32] 	Loss_D: -0.1660 | Loss_G: -0.4198 | Iteration Time: 0.0895 sec
[4/200][20/32] 	Loss_D: -0.1114 | Loss_G: -0.4598 | Iteration Time: 0.0895 sec
[4/200][25/32] 	Loss_D: -0.1390 | Loss_G: -0.4411 | Iteration Time: 0.0960 sec
Training Progress:   2%|▎         | 5/200 [00:25<14:19,  4.41s/it]
[4/200][30/32] 	Loss_D: -0.1584 | Loss_G: -0.4139 | Iteration Time: 0.0920 sec
[5/200][0/32] 	Loss_D: -0.0824 | Loss_G: -0.4792 | Iteration Time: 0.0920 sec
[5/200][5/32] 	Loss_D: -0.1507 | Loss_G: -0.4125 | Iteration Time: 0.0890 sec
[5/200][10/32] 	Loss_D: -0.1320 | Loss_G: -0.4096 | Iteration Time: 0.0895 sec
[5/200][15/32] 	Loss_D: -0.1489 | Loss_G: -0.4092 | Iteration Time: 0.0900 sec
[5/200][20/32] 	Loss_D: -0.1810 | Loss_G: -0.4073 | Iteration Time: 0.0930 sec
[5/200][25/32] 	Loss_D: -0.1939 | Loss_G: -0.4013 | Iteration Time: 0.0920 sec
Training Progress:   3%|▎         | 6/200 [00:29<13:42,  4.24s/it]
[5/200][30/32] 	Loss_D: -0.1260 | Loss_G: -0.4039 | Iteration Time: 0.0960 sec
[6/200][0/32] 	Loss_D: -0.1714 | Loss_G: -0.4112 | Iteration Time: 0.0980 sec
[6/200][5/32] 	Loss_D: -0.1717 | Loss_G: -0.4039 | Iteration Time: 0.0975 sec
[6/200][10/32] 	Loss_D: -0.1860 | Loss_G: -0.4061 | Iteration Time: 0.1240 sec
[6/200][15/32] 	Loss_D: -0.1920 | Loss_G: -0.4048 | Iteration Time: 0.1225 sec
[6/200][20/32] 	Loss_D: -0.1731 | Loss_G: -0.4017 | Iteration Time: 0.1430 sec
[6/200][25/32] 	Loss_D: -0.1674 | Loss_G: -0.4291 | Iteration Time: 0.1470 sec
Training Progress:   4%|▎         | 7/200 [00:34<14:29,  4.50s/it]
[6/200][30/32] 	Loss_D: -0.1190 | Loss_G: -0.4553 | Iteration Time: 0.1425 sec
[7/200][0/32] 	Loss_D: -0.1846 | Loss_G: -0.4096 | Iteration Time: 0.1594 sec
[7/200][5/32] 	Loss_D: -0.1012 | Loss_G: -0.4704 | Iteration Time: 0.1425 sec
[7/200][10/32] 	Loss_D: -0.1240 | Loss_G: -0.4018 | Iteration Time: 0.1485 sec
[7/200][15/32] 	Loss_D: -0.1407 | Loss_G: -0.4041 | Iteration Time: 0.1465 sec
[7/200][20/32] 	Loss_D: -0.1814 | Loss_G: -0.4047 | Iteration Time: 0.1485 sec
[7/200][25/32] 	Loss_D: -0.1868 | Loss_G: -0.4075 | Iteration Time: 0.1436 sec
Training Progress:   4%|▍         | 8/200 [00:40<15:36,  4.88s/it]
[7/200][30/32] 	Loss_D: -0.1759 | Loss_G: -0.4122 | Iteration Time: 0.1474 sec
[8/200][0/32] 	Loss_D: -0.1861 | Loss_G: -0.4060 | Iteration Time: 0.1480 sec
[8/200][5/32] 	Loss_D: -0.1984 | Loss_G: -0.4018 | Iteration Time: 0.1435 sec
[8/200][10/32] 	Loss_D: -0.1305 | Loss_G: -0.4062 | Iteration Time: 0.1450 sec
[8/200][15/32] 	Loss_D: -0.1596 | Loss_G: -0.4129 | Iteration Time: 0.1455 sec
[8/200][20/32] 	Loss_D: -0.1127 | Loss_G: -0.4690 | Iteration Time: 0.1450 sec
[8/200][25/32] 	Loss_D: -0.1211 | Loss_G: -0.4085 | Iteration Time: 0.1440 sec
Training Progress:   4%|▍         | 9/200 [00:45<16:18,  5.12s/it]
[8/200][30/32] 	Loss_D: -0.1634 | Loss_G: -0.4065 | Iteration Time: 0.1445 sec
[9/200][0/32] 	Loss_D: -0.1942 | Loss_G: -0.4026 | Iteration Time: 0.1490 sec
[9/200][5/32] 	Loss_D: -0.1846 | Loss_G: -0.4072 | Iteration Time: 0.1445 sec
[9/200][10/32] 	Loss_D: -0.1217 | Loss_G: -0.4173 | Iteration Time: 0.1433 sec
[9/200][15/32] 	Loss_D: -0.0614 | Loss_G: -0.4365 | Iteration Time: 0.1452 sec
[9/200][20/32] 	Loss_D: -0.1290 | Loss_G: -0.4080 | Iteration Time: 0.1435 sec
[9/200][25/32] 	Loss_D: -0.1917 | Loss_G: -0.4073 | Iteration Time: 0.1445 sec
Training Progress:   5%|▌         | 10/200 [00:51<16:46,  5.30s/it]
[9/200][30/32] 	Loss_D: -0.1980 | Loss_G: -0.4027 | Iteration Time: 0.1440 sec
[10/200][0/32] 	Loss_D: -0.0153 | Loss_G: -0.4066 | Iteration Time: 0.1660 sec
[10/200][5/32] 	Loss_D: -0.1927 | Loss_G: -0.4033 | Iteration Time: 0.1425 sec
[10/200][10/32] 	Loss_D: -0.0726 | Loss_G: -0.4442 | Iteration Time: 0.1440 sec
[10/200][15/32] 	Loss_D: -0.1633 | Loss_G: -0.4118 | Iteration Time: 0.1440 sec
[10/200][20/32] 	Loss_D: -0.0835 | Loss_G: -0.4610 | Iteration Time: 0.1466 sec
[10/200][25/32] 	Loss_D: -0.1793 | Loss_G: -0.4150 | Iteration Time: 0.1455 sec
Training Progress:   6%|▌         | 11/200 [00:57<17:03,  5.42s/it]
[10/200][30/32] 	Loss_D: -0.1700 | Loss_G: -0.4075 | Iteration Time: 0.1455 sec
[11/200][0/32] 	Loss_D: -0.0923 | Loss_G: -0.4736 | Iteration Time: 0.1515 sec
[11/200][5/32] 	Loss_D: -0.1916 | Loss_G: -0.4036 | Iteration Time: 0.1525 sec
[11/200][10/32] 	Loss_D: -0.0929 | Loss_G: -0.4559 | Iteration Time: 0.1451 sec
[11/200][15/32] 	Loss_D: -0.1470 | Loss_G: -0.4225 | Iteration Time: 0.1505 sec
[11/200][20/32] 	Loss_D: -0.1461 | Loss_G: -0.4114 | Iteration Time: 0.1460 sec
[11/200][25/32] 	Loss_D: -0.1790 | Loss_G: -0.4092 | Iteration Time: 0.1455 sec
Training Progress:   6%|▌         | 12/200 [01:03<17:13,  5.50s/it]
[11/200][30/32] 	Loss_D: -0.1562 | Loss_G: -0.4044 | Iteration Time: 0.1435 sec
[12/200][0/32] 	Loss_D: -0.0929 | Loss_G: -0.4146 | Iteration Time: 0.1560 sec
[12/200][5/32] 	Loss_D: -0.1065 | Loss_G: -0.4720 | Iteration Time: 0.1410 sec
[12/200][10/32] 	Loss_D: -0.0950 | Loss_G: -0.4536 | Iteration Time: 0.1435 sec
[12/200][15/32] 	Loss_D: -0.1709 | Loss_G: -0.4204 | Iteration Time: 0.1440 sec
[12/200][20/32] 	Loss_D: -0.1226 | Loss_G: -0.4042 | Iteration Time: 0.1465 sec
[12/200][25/32] 	Loss_D: -0.1816 | Loss_G: -0.4056 | Iteration Time: 0.1445 sec
Training Progress:   6%|▋         | 13/200 [01:08<17:15,  5.54s/it]
[12/200][30/32] 	Loss_D: -0.1481 | Loss_G: -0.4148 | Iteration Time: 0.1440 sec
[13/200][0/32] 	Loss_D: -0.1060 | Loss_G: -0.4132 | Iteration Time: 0.1640 sec
[13/200][5/32] 	Loss_D: -0.0851 | Loss_G: -0.4533 | Iteration Time: 0.1410 sec
[13/200][10/32] 	Loss_D: -0.1341 | Loss_G: -0.4155 | Iteration Time: 0.1460 sec
[13/200][15/32] 	Loss_D: -0.1659 | Loss_G: -0.4013 | Iteration Time: 0.1435 sec
[13/200][20/32] 	Loss_D: -0.1120 | Loss_G: -0.4130 | Iteration Time: 0.1475 sec
[13/200][25/32] 	Loss_D: -0.1060 | Loss_G: -0.4884 | Iteration Time: 0.1470 sec
Training Progress:   7%|▋         | 14/200 [01:14<17:18,  5.58s/it]
[13/200][30/32] 	Loss_D: -0.1708 | Loss_G: -0.4094 | Iteration Time: 0.1450 sec
[14/200][0/32] 	Loss_D: -0.0454 | Loss_G: -0.4342 | Iteration Time: 0.1470 sec
[14/200][5/32] 	Loss_D: -0.1535 | Loss_G: -0.4328 | Iteration Time: 0.1480 sec
[14/200][10/32] 	Loss_D: -0.1658 | Loss_G: -0.4135 | Iteration Time: 0.1430 sec
[14/200][15/32] 	Loss_D: -0.1126 | Loss_G: -0.4669 | Iteration Time: 0.1465 sec
[14/200][20/32] 	Loss_D: -0.0254 | Loss_G: -0.4080 | Iteration Time: 0.1420 sec
[14/200][25/32] 	Loss_D: -0.0904 | Loss_G: -0.4118 | Iteration Time: 0.1490 sec
Training Progress:   8%|▊         | 15/200 [01:19<17:16,  5.60s/it]
[14/200][30/32] 	Loss_D: -0.0563 | Loss_G: -0.4447 | Iteration Time: 0.1415 sec
[15/200][0/32] 	Loss_D: -0.1169 | Loss_G: -0.5044 | Iteration Time: 0.1545 sec
[15/200][5/32] 	Loss_D: -0.0732 | Loss_G: -0.4147 | Iteration Time: 0.1415 sec
[15/200][10/32] 	Loss_D: -0.1519 | Loss_G: -0.4331 | Iteration Time: 0.1411 sec
[15/200][15/32] 	Loss_D: -0.1455 | Loss_G: -0.4149 | Iteration Time: 0.1450 sec
[15/200][20/32] 	Loss_D: -0.1094 | Loss_G: -0.4170 | Iteration Time: 0.1430 sec
Current scores at iteration 500 | FID: 316.298095703125 | IS: 1.9010511636734009
No description has been provided for this image
[15/200][25/32] 	Loss_D: -0.0853 | Loss_G: -0.4914 | Iteration Time: 0.1400 sec
Training Progress:   8%|▊         | 16/200 [01:30<22:09,  7.22s/it]
[15/200][30/32] 	Loss_D: -0.1873 | Loss_G: -0.4055 | Iteration Time: 0.1430 sec
[16/200][0/32] 	Loss_D: -0.1345 | Loss_G: -0.4091 | Iteration Time: 0.1525 sec
[16/200][5/32] 	Loss_D: -0.0589 | Loss_G: -0.4501 | Iteration Time: 0.1410 sec
[16/200][10/32] 	Loss_D: -0.1495 | Loss_G: -0.4212 | Iteration Time: 0.1467 sec
[16/200][15/32] 	Loss_D: -0.1256 | Loss_G: -0.4179 | Iteration Time: 0.1445 sec
[16/200][20/32] 	Loss_D: -0.1570 | Loss_G: -0.4438 | Iteration Time: 0.1445 sec
[16/200][25/32] 	Loss_D: -0.1389 | Loss_G: -0.4069 | Iteration Time: 0.1430 sec
Training Progress:   8%|▊         | 17/200 [01:36<20:35,  6.75s/it]
[16/200][30/32] 	Loss_D: -0.1859 | Loss_G: -0.4032 | Iteration Time: 0.1445 sec
[17/200][0/32] 	Loss_D: -0.1218 | Loss_G: -0.5183 | Iteration Time: 0.1605 sec
[17/200][5/32] 	Loss_D: -0.0872 | Loss_G: -0.5126 | Iteration Time: 0.1460 sec
[17/200][10/32] 	Loss_D: -0.1380 | Loss_G: -0.4197 | Iteration Time: 0.1490 sec
[17/200][15/32] 	Loss_D: -0.1151 | Loss_G: -0.4594 | Iteration Time: 0.1455 sec
[17/200][20/32] 	Loss_D: -0.1512 | Loss_G: -0.4098 | Iteration Time: 0.1445 sec
[17/200][25/32] 	Loss_D: -0.1041 | Loss_G: -0.4704 | Iteration Time: 0.1455 sec
Training Progress:   9%|▉         | 18/200 [01:42<19:29,  6.42s/it]
[17/200][30/32] 	Loss_D: -0.1132 | Loss_G: -0.4197 | Iteration Time: 0.1485 sec
[18/200][0/32] 	Loss_D: -0.1228 | Loss_G: -0.4175 | Iteration Time: 0.1430 sec
[18/200][5/32] 	Loss_D: -0.1596 | Loss_G: -0.4294 | Iteration Time: 0.1440 sec
[18/200][10/32] 	Loss_D: -0.1441 | Loss_G: -0.4239 | Iteration Time: 0.1455 sec
[18/200][15/32] 	Loss_D: -0.1643 | Loss_G: -0.4130 | Iteration Time: 0.1439 sec
[18/200][20/32] 	Loss_D: -0.1116 | Loss_G: -0.4150 | Iteration Time: 0.1416 sec
[18/200][25/32] 	Loss_D: -0.1741 | Loss_G: -0.4082 | Iteration Time: 0.1430 sec
Training Progress:  10%|▉         | 19/200 [01:47<18:38,  6.18s/it]
[18/200][30/32] 	Loss_D: -0.0650 | Loss_G: -0.4448 | Iteration Time: 0.1430 sec
[19/200][0/32] 	Loss_D: -0.1027 | Loss_G: -0.4558 | Iteration Time: 0.1500 sec
[19/200][5/32] 	Loss_D: -0.1180 | Loss_G: -0.4095 | Iteration Time: 0.1465 sec
[19/200][10/32] 	Loss_D: -0.1583 | Loss_G: -0.4159 | Iteration Time: 0.1430 sec
[19/200][15/32] 	Loss_D: -0.0911 | Loss_G: -0.4786 | Iteration Time: 0.1485 sec
[19/200][20/32] 	Loss_D: -0.1560 | Loss_G: -0.4109 | Iteration Time: 0.1450 sec
[19/200][25/32] 	Loss_D: -0.0896 | Loss_G: -0.4507 | Iteration Time: 0.1440 sec
Training Progress:  10%|█         | 20/200 [01:53<18:02,  6.01s/it]
[19/200][30/32] 	Loss_D: -0.0692 | Loss_G: -0.4592 | Iteration Time: 0.1445 sec
[20/200][0/32] 	Loss_D: -0.0784 | Loss_G: -0.4926 | Iteration Time: 0.1485 sec
[20/200][5/32] 	Loss_D: -0.1366 | Loss_G: -0.4069 | Iteration Time: 0.1465 sec
[20/200][10/32] 	Loss_D: -0.1152 | Loss_G: -0.4087 | Iteration Time: 0.1450 sec
[20/200][15/32] 	Loss_D: -0.1185 | Loss_G: -0.4182 | Iteration Time: 0.1445 sec
[20/200][20/32] 	Loss_D: -0.1028 | Loss_G: -0.4921 | Iteration Time: 0.1440 sec
[20/200][25/32] 	Loss_D: -0.1063 | Loss_G: -0.4147 | Iteration Time: 0.1435 sec
Training Progress:  10%|█         | 21/200 [01:59<17:33,  5.89s/it]
[20/200][30/32] 	Loss_D: -0.1275 | Loss_G: -0.4221 | Iteration Time: 0.1435 sec
[21/200][0/32] 	Loss_D: -0.1474 | Loss_G: -0.4107 | Iteration Time: 0.1490 sec
[21/200][5/32] 	Loss_D: -0.0379 | Loss_G: -0.4259 | Iteration Time: 0.1430 sec
[21/200][10/32] 	Loss_D: -0.1134 | Loss_G: -0.4069 | Iteration Time: 0.1430 sec
[21/200][15/32] 	Loss_D: -0.1169 | Loss_G: -0.4743 | Iteration Time: 0.1505 sec
[21/200][20/32] 	Loss_D: -0.1559 | Loss_G: -0.4361 | Iteration Time: 0.1445 sec
[21/200][25/32] 	Loss_D: -0.1244 | Loss_G: -0.4139 | Iteration Time: 0.1447 sec
Training Progress:  11%|█         | 22/200 [02:04<17:13,  5.81s/it]
[21/200][30/32] 	Loss_D: -0.1288 | Loss_G: -0.4591 | Iteration Time: 0.1430 sec
[22/200][0/32] 	Loss_D: -0.1239 | Loss_G: -0.4616 | Iteration Time: 0.1515 sec
[22/200][5/32] 	Loss_D: -0.1204 | Loss_G: -0.4086 | Iteration Time: 0.1405 sec
[22/200][10/32] 	Loss_D: -0.1080 | Loss_G: -0.4444 | Iteration Time: 0.1423 sec
[22/200][15/32] 	Loss_D: -0.0910 | Loss_G: -0.4514 | Iteration Time: 0.1460 sec
[22/200][20/32] 	Loss_D: -0.1312 | Loss_G: -0.4184 | Iteration Time: 0.1445 sec
[22/200][25/32] 	Loss_D: -0.0949 | Loss_G: -0.4148 | Iteration Time: 0.1450 sec
Training Progress:  12%|█▏        | 23/200 [02:10<16:55,  5.74s/it]
[22/200][30/32] 	Loss_D: -0.1316 | Loss_G: -0.4058 | Iteration Time: 0.1425 sec
[23/200][0/32] 	Loss_D: -0.0723 | Loss_G: -0.4202 | Iteration Time: 0.1480 sec
[23/200][5/32] 	Loss_D: -0.1239 | Loss_G: -0.4486 | Iteration Time: 0.1435 sec
[23/200][10/32] 	Loss_D: -0.1149 | Loss_G: -0.4697 | Iteration Time: 0.1440 sec
[23/200][15/32] 	Loss_D: -0.1434 | Loss_G: -0.4158 | Iteration Time: 0.1450 sec
[23/200][20/32] 	Loss_D: -0.0511 | Loss_G: -0.4440 | Iteration Time: 0.1453 sec
[23/200][25/32] 	Loss_D: -0.1158 | Loss_G: -0.4146 | Iteration Time: 0.1415 sec
Training Progress:  12%|█▏        | 24/200 [02:15<16:42,  5.69s/it]
[23/200][30/32] 	Loss_D: -0.1261 | Loss_G: -0.4105 | Iteration Time: 0.1435 sec
[24/200][0/32] 	Loss_D: -0.1284 | Loss_G: -0.4101 | Iteration Time: 0.1490 sec
[24/200][5/32] 	Loss_D: -0.0931 | Loss_G: -0.4126 | Iteration Time: 0.1500 sec
[24/200][10/32] 	Loss_D: -0.1099 | Loss_G: -0.4150 | Iteration Time: 0.1440 sec
[24/200][15/32] 	Loss_D: -0.1027 | Loss_G: -0.4753 | Iteration Time: 0.1450 sec
[24/200][20/32] 	Loss_D: -0.1146 | Loss_G: -0.4614 | Iteration Time: 0.1405 sec
[24/200][25/32] 	Loss_D: -0.1166 | Loss_G: -0.4242 | Iteration Time: 0.1444 sec
Training Progress:  12%|█▎        | 25/200 [02:21<16:30,  5.66s/it]
[24/200][30/32] 	Loss_D: -0.1083 | Loss_G: -0.4726 | Iteration Time: 0.1415 sec
[25/200][0/32] 	Loss_D: -0.1450 | Loss_G: -0.4440 | Iteration Time: 0.1505 sec
[25/200][5/32] 	Loss_D: -0.1385 | Loss_G: -0.4213 | Iteration Time: 0.1435 sec
[25/200][10/32] 	Loss_D: -0.1039 | Loss_G: -0.4134 | Iteration Time: 0.1435 sec
[25/200][15/32] 	Loss_D: -0.1245 | Loss_G: -0.4930 | Iteration Time: 0.1430 sec
[25/200][20/32] 	Loss_D: -0.1286 | Loss_G: -0.4384 | Iteration Time: 0.1440 sec
[25/200][25/32] 	Loss_D: -0.1123 | Loss_G: -0.4562 | Iteration Time: 0.1425 sec
Training Progress:  13%|█▎        | 26/200 [02:27<16:21,  5.64s/it]
[25/200][30/32] 	Loss_D: -0.0806 | Loss_G: -0.4207 | Iteration Time: 0.1445 sec
[26/200][0/32] 	Loss_D: -0.0880 | Loss_G: -0.4179 | Iteration Time: 0.1510 sec
[26/200][5/32] 	Loss_D: -0.0932 | Loss_G: -0.4137 | Iteration Time: 0.1440 sec
[26/200][10/32] 	Loss_D: -0.1093 | Loss_G: -0.4503 | Iteration Time: 0.1435 sec
[26/200][15/32] 	Loss_D: -0.1112 | Loss_G: -0.4090 | Iteration Time: 0.1445 sec
[26/200][20/32] 	Loss_D: -0.1081 | Loss_G: -0.4411 | Iteration Time: 0.1440 sec
[26/200][25/32] 	Loss_D: -0.0918 | Loss_G: -0.4812 | Iteration Time: 0.1440 sec
Training Progress:  14%|█▎        | 27/200 [02:32<16:13,  5.63s/it]
[26/200][30/32] 	Loss_D: -0.1211 | Loss_G: -0.4332 | Iteration Time: 0.1422 sec
[27/200][0/32] 	Loss_D: -0.1023 | Loss_G: -0.4743 | Iteration Time: 0.1491 sec
[27/200][5/32] 	Loss_D: -0.1169 | Loss_G: -0.4690 | Iteration Time: 0.1470 sec
[27/200][10/32] 	Loss_D: -0.0857 | Loss_G: -0.4785 | Iteration Time: 0.1445 sec
[27/200][15/32] 	Loss_D: -0.1115 | Loss_G: -0.4180 | Iteration Time: 0.1435 sec
[27/200][20/32] 	Loss_D: -0.0997 | Loss_G: -0.4133 | Iteration Time: 0.1440 sec
[27/200][25/32] 	Loss_D: -0.0809 | Loss_G: -0.4624 | Iteration Time: 0.1445 sec
Training Progress:  14%|█▍        | 28/200 [02:38<16:07,  5.63s/it]
[27/200][30/32] 	Loss_D: -0.1052 | Loss_G: -0.4774 | Iteration Time: 0.1443 sec
[28/200][0/32] 	Loss_D: -0.1003 | Loss_G: -0.4564 | Iteration Time: 0.1470 sec
[28/200][5/32] 	Loss_D: -0.1018 | Loss_G: -0.4789 | Iteration Time: 0.1440 sec
[28/200][10/32] 	Loss_D: -0.0930 | Loss_G: -0.4208 | Iteration Time: 0.1415 sec
[28/200][15/32] 	Loss_D: -0.1102 | Loss_G: -0.4119 | Iteration Time: 0.1450 sec
[28/200][20/32] 	Loss_D: -0.1066 | Loss_G: -0.4120 | Iteration Time: 0.1430 sec
[28/200][25/32] 	Loss_D: -0.1047 | Loss_G: -0.4600 | Iteration Time: 0.1450 sec
Training Progress:  14%|█▍        | 29/200 [02:43<15:59,  5.61s/it]
[28/200][30/32] 	Loss_D: -0.1177 | Loss_G: -0.4123 | Iteration Time: 0.1500 sec
[29/200][0/32] 	Loss_D: -0.1124 | Loss_G: -0.4134 | Iteration Time: 0.1460 sec
[29/200][5/32] 	Loss_D: -0.0980 | Loss_G: -0.4620 | Iteration Time: 0.1460 sec
[29/200][10/32] 	Loss_D: -0.1322 | Loss_G: -0.4139 | Iteration Time: 0.1450 sec
[29/200][15/32] 	Loss_D: -0.1264 | Loss_G: -0.4121 | Iteration Time: 0.1420 sec
[29/200][20/32] 	Loss_D: -0.0587 | Loss_G: -0.4444 | Iteration Time: 0.1435 sec
[29/200][25/32] 	Loss_D: -0.1133 | Loss_G: -0.4355 | Iteration Time: 0.1391 sec
Training Progress:  15%|█▌        | 30/200 [02:49<15:52,  5.60s/it]
[29/200][30/32] 	Loss_D: -0.0664 | Loss_G: -0.4154 | Iteration Time: 0.1460 sec
[30/200][0/32] 	Loss_D: -0.1064 | Loss_G: -0.4100 | Iteration Time: 0.1440 sec
[30/200][5/32] 	Loss_D: -0.0840 | Loss_G: -0.4801 | Iteration Time: 0.1416 sec
[30/200][10/32] 	Loss_D: -0.1324 | Loss_G: -0.4422 | Iteration Time: 0.1409 sec
[30/200][15/32] 	Loss_D: -0.1052 | Loss_G: -0.4517 | Iteration Time: 0.1438 sec
[30/200][20/32] 	Loss_D: -0.0590 | Loss_G: -0.4825 | Iteration Time: 0.1505 sec
[30/200][25/32] 	Loss_D: -0.1010 | Loss_G: -0.4077 | Iteration Time: 0.1435 sec
Training Progress:  16%|█▌        | 31/200 [02:55<15:45,  5.59s/it]
[30/200][30/32] 	Loss_D: -0.1350 | Loss_G: -0.4297 | Iteration Time: 0.1435 sec
[31/200][0/32] 	Loss_D: -0.1302 | Loss_G: -0.4071 | Iteration Time: 0.1475 sec
[31/200][5/32] 	Loss_D: -0.0946 | Loss_G: -0.4834 | Iteration Time: 0.1440 sec
Current scores at iteration 1000 | FID: 300.595458984375 | IS: 2.152280330657959
No description has been provided for this image
[31/200][10/32] 	Loss_D: -0.0760 | Loss_G: -0.4654 | Iteration Time: 0.1420 sec
[31/200][15/32] 	Loss_D: -0.0789 | Loss_G: -0.4203 | Iteration Time: 0.1420 sec
[31/200][20/32] 	Loss_D: -0.1160 | Loss_G: -0.4095 | Iteration Time: 0.1405 sec
[31/200][25/32] 	Loss_D: -0.1083 | Loss_G: -0.4276 | Iteration Time: 0.1600 sec
Training Progress:  16%|█▌        | 32/200 [03:05<20:08,  7.19s/it]
[31/200][30/32] 	Loss_D: -0.0804 | Loss_G: -0.4237 | Iteration Time: 0.1405 sec
[32/200][0/32] 	Loss_D: -0.0721 | Loss_G: -0.4259 | Iteration Time: 0.1525 sec
[32/200][5/32] 	Loss_D: -0.0812 | Loss_G: -0.4779 | Iteration Time: 0.1395 sec
[32/200][10/32] 	Loss_D: -0.1007 | Loss_G: -0.4195 | Iteration Time: 0.1415 sec
[32/200][15/32] 	Loss_D: -0.1116 | Loss_G: -0.4888 | Iteration Time: 0.1425 sec
[32/200][20/32] 	Loss_D: -0.0920 | Loss_G: -0.4757 | Iteration Time: 0.1450 sec
[32/200][25/32] 	Loss_D: -0.1301 | Loss_G: -0.4289 | Iteration Time: 0.1475 sec
Training Progress:  16%|█▋        | 33/200 [03:11<18:39,  6.71s/it]
[32/200][30/32] 	Loss_D: -0.0940 | Loss_G: -0.4206 | Iteration Time: 0.1425 sec
[33/200][0/32] 	Loss_D: -0.1153 | Loss_G: -0.4114 | Iteration Time: 0.1475 sec
[33/200][5/32] 	Loss_D: -0.1078 | Loss_G: -0.4426 | Iteration Time: 0.1415 sec
[33/200][10/32] 	Loss_D: -0.0832 | Loss_G: -0.4987 | Iteration Time: 0.1430 sec
[33/200][15/32] 	Loss_D: -0.1041 | Loss_G: -0.4077 | Iteration Time: 0.1450 sec
[33/200][20/32] 	Loss_D: -0.1018 | Loss_G: -0.4074 | Iteration Time: 0.1445 sec
[33/200][25/32] 	Loss_D: -0.1037 | Loss_G: -0.4802 | Iteration Time: 0.1420 sec
Training Progress:  17%|█▋        | 34/200 [03:17<17:32,  6.34s/it]
[33/200][30/32] 	Loss_D: -0.1108 | Loss_G: -0.4592 | Iteration Time: 0.1415 sec
[34/200][0/32] 	Loss_D: -0.0872 | Loss_G: -0.4673 | Iteration Time: 0.1530 sec
[34/200][5/32] 	Loss_D: -0.0687 | Loss_G: -0.4808 | Iteration Time: 0.1435 sec
[34/200][10/32] 	Loss_D: -0.1005 | Loss_G: -0.4154 | Iteration Time: 0.1455 sec
[34/200][15/32] 	Loss_D: -0.0916 | Loss_G: -0.4832 | Iteration Time: 0.1430 sec
[34/200][20/32] 	Loss_D: -0.1196 | Loss_G: -0.4177 | Iteration Time: 0.1450 sec
[34/200][25/32] 	Loss_D: -0.0493 | Loss_G: -0.4516 | Iteration Time: 0.1445 sec
Training Progress:  18%|█▊        | 35/200 [03:22<16:47,  6.11s/it]
[34/200][30/32] 	Loss_D: -0.0968 | Loss_G: -0.4148 | Iteration Time: 0.1420 sec
[35/200][0/32] 	Loss_D: -0.0846 | Loss_G: -0.4191 | Iteration Time: 0.1585 sec
[35/200][5/32] 	Loss_D: -0.1058 | Loss_G: -0.4123 | Iteration Time: 0.1415 sec
[35/200][10/32] 	Loss_D: -0.0853 | Loss_G: -0.4115 | Iteration Time: 0.1445 sec
[35/200][15/32] 	Loss_D: -0.1189 | Loss_G: -0.4158 | Iteration Time: 0.1425 sec
[35/200][20/32] 	Loss_D: -0.1031 | Loss_G: -0.4376 | Iteration Time: 0.1420 sec
[35/200][25/32] 	Loss_D: -0.0966 | Loss_G: -0.4664 | Iteration Time: 0.1455 sec
Training Progress:  18%|█▊        | 36/200 [03:28<16:13,  5.93s/it]
[35/200][30/32] 	Loss_D: -0.1075 | Loss_G: -0.4166 | Iteration Time: 0.1435 sec
[36/200][0/32] 	Loss_D: -0.1002 | Loss_G: -0.4663 | Iteration Time: 0.1525 sec
[36/200][5/32] 	Loss_D: -0.1105 | Loss_G: -0.4096 | Iteration Time: 0.1435 sec
[36/200][10/32] 	Loss_D: -0.0929 | Loss_G: -0.4688 | Iteration Time: 0.1415 sec
[36/200][15/32] 	Loss_D: -0.0875 | Loss_G: -0.4251 | Iteration Time: 0.1460 sec
[36/200][20/32] 	Loss_D: -0.0843 | Loss_G: -0.5043 | Iteration Time: 0.1435 sec
[36/200][25/32] 	Loss_D: -0.0819 | Loss_G: -0.4711 | Iteration Time: 0.1430 sec
Training Progress:  18%|█▊        | 37/200 [03:33<15:48,  5.82s/it]
[36/200][30/32] 	Loss_D: -0.0771 | Loss_G: -0.5002 | Iteration Time: 0.1430 sec
[37/200][0/32] 	Loss_D: -0.0955 | Loss_G: -0.4132 | Iteration Time: 0.1480 sec
[37/200][5/32] 	Loss_D: -0.0887 | Loss_G: -0.4121 | Iteration Time: 0.1410 sec
[37/200][10/32] 	Loss_D: -0.1016 | Loss_G: -0.4718 | Iteration Time: 0.1435 sec
[37/200][15/32] 	Loss_D: -0.0950 | Loss_G: -0.4188 | Iteration Time: 0.1440 sec
[37/200][20/32] 	Loss_D: -0.1116 | Loss_G: -0.5193 | Iteration Time: 0.1435 sec
[37/200][25/32] 	Loss_D: -0.1016 | Loss_G: -0.4170 | Iteration Time: 0.1430 sec
Training Progress:  19%|█▉        | 38/200 [03:39<15:28,  5.73s/it]
[37/200][30/32] 	Loss_D: -0.0872 | Loss_G: -0.4721 | Iteration Time: 0.1435 sec
[38/200][0/32] 	Loss_D: -0.1076 | Loss_G: -0.4690 | Iteration Time: 0.1596 sec
[38/200][5/32] 	Loss_D: -0.0876 | Loss_G: -0.4169 | Iteration Time: 0.1424 sec
[38/200][10/32] 	Loss_D: -0.1202 | Loss_G: -0.4140 | Iteration Time: 0.1425 sec
[38/200][15/32] 	Loss_D: -0.0919 | Loss_G: -0.4703 | Iteration Time: 0.1420 sec
[38/200][20/32] 	Loss_D: -0.0901 | Loss_G: -0.4089 | Iteration Time: 0.1425 sec
[38/200][25/32] 	Loss_D: -0.0995 | Loss_G: -0.4597 | Iteration Time: 0.1505 sec
Training Progress:  20%|█▉        | 39/200 [03:44<15:13,  5.68s/it]
[38/200][30/32] 	Loss_D: -0.0714 | Loss_G: -0.4200 | Iteration Time: 0.1450 sec
[39/200][0/32] 	Loss_D: -0.0889 | Loss_G: -0.4225 | Iteration Time: 0.1525 sec
[39/200][5/32] 	Loss_D: -0.1151 | Loss_G: -0.4458 | Iteration Time: 0.1396 sec
[39/200][10/32] 	Loss_D: -0.0954 | Loss_G: -0.4109 | Iteration Time: 0.1401 sec
[39/200][15/32] 	Loss_D: -0.1029 | Loss_G: -0.4324 | Iteration Time: 0.1465 sec
[39/200][20/32] 	Loss_D: -0.0585 | Loss_G: -0.4943 | Iteration Time: 0.1440 sec
[39/200][25/32] 	Loss_D: -0.0897 | Loss_G: -0.4272 | Iteration Time: 0.1435 sec
Training Progress:  20%|██        | 40/200 [03:50<15:01,  5.64s/it]
[39/200][30/32] 	Loss_D: -0.0802 | Loss_G: -0.5299 | Iteration Time: 0.1430 sec
[40/200][0/32] 	Loss_D: -0.0654 | Loss_G: -0.4934 | Iteration Time: 0.1480 sec
[40/200][5/32] 	Loss_D: -0.0922 | Loss_G: -0.4891 | Iteration Time: 0.1465 sec
[40/200][10/32] 	Loss_D: -0.1135 | Loss_G: -0.4180 | Iteration Time: 0.1402 sec
[40/200][15/32] 	Loss_D: -0.0873 | Loss_G: -0.4662 | Iteration Time: 0.1395 sec
[40/200][20/32] 	Loss_D: -0.0815 | Loss_G: -0.4172 | Iteration Time: 0.1485 sec
[40/200][25/32] 	Loss_D: -0.0940 | Loss_G: -0.4830 | Iteration Time: 0.1446 sec
Training Progress:  20%|██        | 41/200 [03:55<14:51,  5.61s/it]
[40/200][30/32] 	Loss_D: -0.0820 | Loss_G: -0.4245 | Iteration Time: 0.1445 sec
[41/200][0/32] 	Loss_D: -0.1053 | Loss_G: -0.4879 | Iteration Time: 0.1460 sec
[41/200][5/32] 	Loss_D: -0.0723 | Loss_G: -0.4231 | Iteration Time: 0.1440 sec
[41/200][10/32] 	Loss_D: -0.0882 | Loss_G: -0.4217 | Iteration Time: 0.1445 sec
[41/200][15/32] 	Loss_D: -0.1080 | Loss_G: -0.4448 | Iteration Time: 0.1425 sec
[41/200][20/32] 	Loss_D: -0.1124 | Loss_G: -0.4180 | Iteration Time: 0.1445 sec
[41/200][25/32] 	Loss_D: -0.0832 | Loss_G: -0.4713 | Iteration Time: 0.1430 sec
Training Progress:  21%|██        | 42/200 [04:01<14:44,  5.60s/it]
[41/200][30/32] 	Loss_D: -0.0493 | Loss_G: -0.4333 | Iteration Time: 0.1435 sec
[42/200][0/32] 	Loss_D: -0.1061 | Loss_G: -0.4187 | Iteration Time: 0.1641 sec
[42/200][5/32] 	Loss_D: -0.0836 | Loss_G: -0.4176 | Iteration Time: 0.1450 sec
[42/200][10/32] 	Loss_D: -0.0858 | Loss_G: -0.4889 | Iteration Time: 0.1470 sec
[42/200][15/32] 	Loss_D: -0.0863 | Loss_G: -0.4188 | Iteration Time: 0.1425 sec
[42/200][20/32] 	Loss_D: -0.0934 | Loss_G: -0.4841 | Iteration Time: 0.1455 sec
[42/200][25/32] 	Loss_D: -0.0865 | Loss_G: -0.4241 | Iteration Time: 0.1431 sec
Training Progress:  22%|██▏       | 43/200 [04:06<14:37,  5.59s/it]
[42/200][30/32] 	Loss_D: -0.0821 | Loss_G: -0.4882 | Iteration Time: 0.1430 sec
[43/200][0/32] 	Loss_D: -0.0509 | Loss_G: -0.4619 | Iteration Time: 0.1550 sec
[43/200][5/32] 	Loss_D: -0.1017 | Loss_G: -0.4294 | Iteration Time: 0.1418 sec
[43/200][10/32] 	Loss_D: -0.0938 | Loss_G: -0.4961 | Iteration Time: 0.1450 sec
[43/200][15/32] 	Loss_D: -0.0948 | Loss_G: -0.4947 | Iteration Time: 0.1440 sec
[43/200][20/32] 	Loss_D: -0.0827 | Loss_G: -0.4212 | Iteration Time: 0.1435 sec
[43/200][25/32] 	Loss_D: -0.0739 | Loss_G: -0.4194 | Iteration Time: 0.1455 sec
Training Progress:  22%|██▏       | 44/200 [04:12<14:29,  5.57s/it]
[43/200][30/32] 	Loss_D: -0.0963 | Loss_G: -0.4709 | Iteration Time: 0.1420 sec
[44/200][0/32] 	Loss_D: -0.0855 | Loss_G: -0.4699 | Iteration Time: 0.1475 sec
[44/200][5/32] 	Loss_D: -0.0900 | Loss_G: -0.4797 | Iteration Time: 0.1415 sec
[44/200][10/32] 	Loss_D: -0.0842 | Loss_G: -0.4135 | Iteration Time: 0.1405 sec
[44/200][15/32] 	Loss_D: -0.1061 | Loss_G: -0.4739 | Iteration Time: 0.1455 sec
[44/200][20/32] 	Loss_D: -0.0949 | Loss_G: -0.4670 | Iteration Time: 0.1420 sec
[44/200][25/32] 	Loss_D: -0.0664 | Loss_G: -0.4215 | Iteration Time: 0.1425 sec
Training Progress:  22%|██▎       | 45/200 [04:18<14:22,  5.57s/it]
[44/200][30/32] 	Loss_D: -0.1079 | Loss_G: -0.4316 | Iteration Time: 0.1480 sec
[45/200][0/32] 	Loss_D: -0.0867 | Loss_G: -0.4958 | Iteration Time: 0.1450 sec
[45/200][5/32] 	Loss_D: -0.0868 | Loss_G: -0.4222 | Iteration Time: 0.1420 sec
[45/200][10/32] 	Loss_D: -0.0666 | Loss_G: -0.4255 | Iteration Time: 0.1430 sec
[45/200][15/32] 	Loss_D: -0.0993 | Loss_G: -0.4607 | Iteration Time: 0.1430 sec
[45/200][20/32] 	Loss_D: -0.0946 | Loss_G: -0.4760 | Iteration Time: 0.1450 sec
[45/200][25/32] 	Loss_D: -0.1037 | Loss_G: -0.4339 | Iteration Time: 0.1435 sec
Training Progress:  23%|██▎       | 46/200 [04:23<14:15,  5.55s/it]
[45/200][30/32] 	Loss_D: -0.0981 | Loss_G: -0.4734 | Iteration Time: 0.1420 sec
[46/200][0/32] 	Loss_D: -0.0903 | Loss_G: -0.4822 | Iteration Time: 0.1607 sec
[46/200][5/32] 	Loss_D: -0.0714 | Loss_G: -0.4307 | Iteration Time: 0.1425 sec
[46/200][10/32] 	Loss_D: -0.0514 | Loss_G: -0.4994 | Iteration Time: 0.1445 sec
[46/200][15/32] 	Loss_D: -0.0883 | Loss_G: -0.4927 | Iteration Time: 0.1425 sec
[46/200][20/32] 	Loss_D: -0.0992 | Loss_G: -0.4100 | Iteration Time: 0.1415 sec
[46/200][25/32] 	Loss_D: -0.0591 | Loss_G: -0.4678 | Iteration Time: 0.1475 sec
Current scores at iteration 1500 | FID: 205.14955139160156 | IS: 2.455692768096924
No description has been provided for this image
Training Progress:  24%|██▎       | 47/200 [04:34<18:18,  7.18s/it]
[46/200][30/32] 	Loss_D: -0.1065 | Loss_G: -0.4136 | Iteration Time: 0.1385 sec
[47/200][0/32] 	Loss_D: -0.0996 | Loss_G: -0.4376 | Iteration Time: 0.1590 sec
[47/200][5/32] 	Loss_D: -0.0969 | Loss_G: -0.4693 | Iteration Time: 0.1420 sec
[47/200][10/32] 	Loss_D: -0.0727 | Loss_G: -0.4622 | Iteration Time: 0.1430 sec
[47/200][15/32] 	Loss_D: -0.0603 | Loss_G: -0.4962 | Iteration Time: 0.1405 sec
[47/200][20/32] 	Loss_D: -0.1055 | Loss_G: -0.4656 | Iteration Time: 0.1395 sec
[47/200][25/32] 	Loss_D: -0.0996 | Loss_G: -0.4163 | Iteration Time: 0.1425 sec
Training Progress:  24%|██▍       | 48/200 [04:40<16:57,  6.69s/it]
[47/200][30/32] 	Loss_D: -0.0865 | Loss_G: -0.4261 | Iteration Time: 0.1430 sec
[48/200][0/32] 	Loss_D: -0.0907 | Loss_G: -0.4632 | Iteration Time: 0.1475 sec
[48/200][5/32] 	Loss_D: -0.0966 | Loss_G: -0.4374 | Iteration Time: 0.1420 sec
[48/200][10/32] 	Loss_D: -0.0808 | Loss_G: -0.4266 | Iteration Time: 0.1410 sec
[48/200][15/32] 	Loss_D: -0.0845 | Loss_G: -0.5066 | Iteration Time: 0.1430 sec
[48/200][20/32] 	Loss_D: -0.0732 | Loss_G: -0.4250 | Iteration Time: 0.1415 sec
[48/200][25/32] 	Loss_D: -0.0901 | Loss_G: -0.4811 | Iteration Time: 0.1415 sec
Training Progress:  24%|██▍       | 49/200 [04:45<15:55,  6.33s/it]
[48/200][30/32] 	Loss_D: -0.0732 | Loss_G: -0.4275 | Iteration Time: 0.1450 sec
[49/200][0/32] 	Loss_D: -0.0700 | Loss_G: -0.4282 | Iteration Time: 0.1600 sec
[49/200][5/32] 	Loss_D: -0.0664 | Loss_G: -0.4191 | Iteration Time: 0.1415 sec
[49/200][10/32] 	Loss_D: -0.0849 | Loss_G: -0.4711 | Iteration Time: 0.1435 sec
[49/200][15/32] 	Loss_D: -0.0860 | Loss_G: -0.4245 | Iteration Time: 0.1400 sec
[49/200][20/32] 	Loss_D: -0.0891 | Loss_G: -0.4102 | Iteration Time: 0.1410 sec
[49/200][25/32] 	Loss_D: -0.0949 | Loss_G: -0.5068 | Iteration Time: 0.1445 sec
Training Progress:  25%|██▌       | 50/200 [04:51<15:10,  6.07s/it]
[49/200][30/32] 	Loss_D: -0.0945 | Loss_G: -0.4807 | Iteration Time: 0.1400 sec
[50/200][0/32] 	Loss_D: -0.0927 | Loss_G: -0.4735 | Iteration Time: 0.1495 sec
[50/200][5/32] 	Loss_D: -0.1210 | Loss_G: -0.4179 | Iteration Time: 0.1450 sec
[50/200][10/32] 	Loss_D: -0.1210 | Loss_G: -0.4430 | Iteration Time: 0.1400 sec
[50/200][15/32] 	Loss_D: -0.0772 | Loss_G: -0.4189 | Iteration Time: 0.1445 sec
[50/200][20/32] 	Loss_D: -0.0772 | Loss_G: -0.4180 | Iteration Time: 0.1450 sec
[50/200][25/32] 	Loss_D: -0.0988 | Loss_G: -0.4793 | Iteration Time: 0.1430 sec
Training Progress:  26%|██▌       | 51/200 [04:56<14:39,  5.90s/it]
[50/200][30/32] 	Loss_D: -0.0799 | Loss_G: -0.4806 | Iteration Time: 0.1409 sec
[51/200][0/32] 	Loss_D: -0.0899 | Loss_G: -0.4688 | Iteration Time: 0.1550 sec
[51/200][5/32] 	Loss_D: -0.0909 | Loss_G: -0.4177 | Iteration Time: 0.1395 sec
[51/200][10/32] 	Loss_D: -0.0907 | Loss_G: -0.4618 | Iteration Time: 0.1415 sec
[51/200][15/32] 	Loss_D: -0.1178 | Loss_G: -0.4290 | Iteration Time: 0.1420 sec
[51/200][20/32] 	Loss_D: -0.1021 | Loss_G: -0.4280 | Iteration Time: 0.1425 sec
[51/200][25/32] 	Loss_D: -0.0827 | Loss_G: -0.4887 | Iteration Time: 0.1435 sec
Training Progress:  26%|██▌       | 52/200 [05:02<14:16,  5.78s/it]
[51/200][30/32] 	Loss_D: -0.0899 | Loss_G: -0.4283 | Iteration Time: 0.1425 sec
[52/200][0/32] 	Loss_D: -0.0637 | Loss_G: -0.4317 | Iteration Time: 0.1410 sec
[52/200][5/32] 	Loss_D: -0.0854 | Loss_G: -0.4633 | Iteration Time: 0.1480 sec
[52/200][10/32] 	Loss_D: -0.0855 | Loss_G: -0.4189 | Iteration Time: 0.1415 sec
[52/200][15/32] 	Loss_D: -0.0541 | Loss_G: -0.4639 | Iteration Time: 0.1445 sec
[52/200][20/32] 	Loss_D: -0.0739 | Loss_G: -0.4358 | Iteration Time: 0.1410 sec
[52/200][25/32] 	Loss_D: -0.0824 | Loss_G: -0.4856 | Iteration Time: 0.1455 sec
Training Progress:  26%|██▋       | 53/200 [05:07<14:00,  5.71s/it]
[52/200][30/32] 	Loss_D: -0.0747 | Loss_G: -0.4251 | Iteration Time: 0.1495 sec
[53/200][0/32] 	Loss_D: -0.1172 | Loss_G: -0.4147 | Iteration Time: 0.1450 sec
[53/200][5/32] 	Loss_D: -0.0977 | Loss_G: -0.4255 | Iteration Time: 0.1432 sec
[53/200][10/32] 	Loss_D: -0.0783 | Loss_G: -0.4213 | Iteration Time: 0.1410 sec
[53/200][15/32] 	Loss_D: -0.0882 | Loss_G: -0.4609 | Iteration Time: 0.1425 sec
[53/200][20/32] 	Loss_D: -0.0917 | Loss_G: -0.4196 | Iteration Time: 0.1460 sec
[53/200][25/32] 	Loss_D: -0.0438 | Loss_G: -0.4861 | Iteration Time: 0.1450 sec
Training Progress:  27%|██▋       | 54/200 [05:13<13:47,  5.67s/it]
[53/200][30/32] 	Loss_D: -0.1222 | Loss_G: -0.4746 | Iteration Time: 0.1435 sec
[54/200][0/32] 	Loss_D: -0.1023 | Loss_G: -0.4702 | Iteration Time: 0.1595 sec
[54/200][5/32] 	Loss_D: -0.0903 | Loss_G: -0.4163 | Iteration Time: 0.1445 sec
[54/200][10/32] 	Loss_D: -0.0924 | Loss_G: -0.4818 | Iteration Time: 0.1450 sec
[54/200][15/32] 	Loss_D: -0.0989 | Loss_G: -0.4724 | Iteration Time: 0.1425 sec
[54/200][20/32] 	Loss_D: -0.0761 | Loss_G: -0.4904 | Iteration Time: 0.1455 sec
[54/200][25/32] 	Loss_D: -0.0801 | Loss_G: -0.4221 | Iteration Time: 0.1430 sec
Training Progress:  28%|██▊       | 55/200 [05:18<13:36,  5.63s/it]
[54/200][30/32] 	Loss_D: -0.1069 | Loss_G: -0.4632 | Iteration Time: 0.1415 sec
[55/200][0/32] 	Loss_D: -0.0723 | Loss_G: -0.4850 | Iteration Time: 0.1526 sec
[55/200][5/32] 	Loss_D: -0.0855 | Loss_G: -0.4683 | Iteration Time: 0.1430 sec
[55/200][10/32] 	Loss_D: -0.0799 | Loss_G: -0.4232 | Iteration Time: 0.1450 sec
[55/200][15/32] 	Loss_D: -0.0764 | Loss_G: -0.4834 | Iteration Time: 0.1440 sec
[55/200][20/32] 	Loss_D: -0.0669 | Loss_G: -0.4818 | Iteration Time: 0.1460 sec
[55/200][25/32] 	Loss_D: -0.0895 | Loss_G: -0.4666 | Iteration Time: 0.1460 sec
Training Progress:  28%|██▊       | 56/200 [05:24<13:28,  5.61s/it]
[55/200][30/32] 	Loss_D: -0.0778 | Loss_G: -0.4279 | Iteration Time: 0.1438 sec
[56/200][0/32] 	Loss_D: -0.0842 | Loss_G: -0.4238 | Iteration Time: 0.1490 sec
[56/200][5/32] 	Loss_D: -0.0869 | Loss_G: -0.4777 | Iteration Time: 0.1440 sec
[56/200][10/32] 	Loss_D: -0.0914 | Loss_G: -0.4712 | Iteration Time: 0.1420 sec
[56/200][15/32] 	Loss_D: -0.0785 | Loss_G: -0.4668 | Iteration Time: 0.1495 sec
[56/200][20/32] 	Loss_D: -0.0762 | Loss_G: -0.4208 | Iteration Time: 0.1430 sec
[56/200][25/32] 	Loss_D: -0.0774 | Loss_G: -0.4294 | Iteration Time: 0.1450 sec
Training Progress:  28%|██▊       | 57/200 [05:29<13:20,  5.60s/it]
[56/200][30/32] 	Loss_D: -0.0908 | Loss_G: -0.4195 | Iteration Time: 0.1420 sec
[57/200][0/32] 	Loss_D: -0.0980 | Loss_G: -0.4142 | Iteration Time: 0.1480 sec
[57/200][5/32] 	Loss_D: -0.0805 | Loss_G: -0.4785 | Iteration Time: 0.1455 sec
[57/200][10/32] 	Loss_D: -0.0941 | Loss_G: -0.4647 | Iteration Time: 0.1405 sec
[57/200][15/32] 	Loss_D: -0.0878 | Loss_G: -0.4193 | Iteration Time: 0.1440 sec
[57/200][20/32] 	Loss_D: -0.0836 | Loss_G: -0.4724 | Iteration Time: 0.1429 sec
[57/200][25/32] 	Loss_D: -0.0815 | Loss_G: -0.4214 | Iteration Time: 0.1440 sec
Training Progress:  29%|██▉       | 58/200 [05:35<13:13,  5.59s/it]
[57/200][30/32] 	Loss_D: -0.0839 | Loss_G: -0.4883 | Iteration Time: 0.1495 sec
[58/200][0/32] 	Loss_D: -0.0807 | Loss_G: -0.4657 | Iteration Time: 0.1435 sec
[58/200][5/32] 	Loss_D: -0.1002 | Loss_G: -0.4213 | Iteration Time: 0.1460 sec
[58/200][10/32] 	Loss_D: -0.0807 | Loss_G: -0.4335 | Iteration Time: 0.1407 sec
[58/200][15/32] 	Loss_D: -0.0611 | Loss_G: -0.4245 | Iteration Time: 0.1450 sec
[58/200][20/32] 	Loss_D: -0.0845 | Loss_G: -0.4151 | Iteration Time: 0.1455 sec
[58/200][25/32] 	Loss_D: -0.0965 | Loss_G: -0.4702 | Iteration Time: 0.1410 sec
Training Progress:  30%|██▉       | 59/200 [05:40<13:05,  5.57s/it]
[58/200][30/32] 	Loss_D: -0.0772 | Loss_G: -0.4192 | Iteration Time: 0.1410 sec
[59/200][0/32] 	Loss_D: -0.0897 | Loss_G: -0.4945 | Iteration Time: 0.1595 sec
[59/200][5/32] 	Loss_D: -0.0765 | Loss_G: -0.4976 | Iteration Time: 0.1425 sec
[59/200][10/32] 	Loss_D: -0.0732 | Loss_G: -0.4934 | Iteration Time: 0.1450 sec
[59/200][15/32] 	Loss_D: -0.0620 | Loss_G: -0.4973 | Iteration Time: 0.1430 sec
[59/200][20/32] 	Loss_D: -0.0883 | Loss_G: -0.4579 | Iteration Time: 0.1435 sec
[59/200][25/32] 	Loss_D: -0.0919 | Loss_G: -0.4726 | Iteration Time: 0.1430 sec
Training Progress:  30%|███       | 60/200 [05:46<12:59,  5.57s/it]
[59/200][30/32] 	Loss_D: -0.0895 | Loss_G: -0.4228 | Iteration Time: 0.1430 sec
[60/200][0/32] 	Loss_D: -0.1014 | Loss_G: -0.4219 | Iteration Time: 0.1530 sec
[60/200][5/32] 	Loss_D: -0.0795 | Loss_G: -0.4842 | Iteration Time: 0.1440 sec
[60/200][10/32] 	Loss_D: -0.0981 | Loss_G: -0.4526 | Iteration Time: 0.1440 sec
[60/200][15/32] 	Loss_D: -0.0655 | Loss_G: -0.4236 | Iteration Time: 0.1415 sec
[60/200][20/32] 	Loss_D: -0.0782 | Loss_G: -0.4804 | Iteration Time: 0.1440 sec
[60/200][25/32] 	Loss_D: -0.0789 | Loss_G: -0.4368 | Iteration Time: 0.1480 sec
Training Progress:  30%|███       | 61/200 [05:52<12:54,  5.57s/it]
[60/200][30/32] 	Loss_D: -0.0651 | Loss_G: -0.4201 | Iteration Time: 0.1440 sec
[61/200][0/32] 	Loss_D: -0.0936 | Loss_G: -0.4267 | Iteration Time: 0.1520 sec
[61/200][5/32] 	Loss_D: -0.0642 | Loss_G: -0.4800 | Iteration Time: 0.1424 sec
[61/200][10/32] 	Loss_D: -0.0982 | Loss_G: -0.4814 | Iteration Time: 0.1400 sec
[61/200][15/32] 	Loss_D: -0.0797 | Loss_G: -0.4277 | Iteration Time: 0.1445 sec
[61/200][20/32] 	Loss_D: -0.0833 | Loss_G: -0.4255 | Iteration Time: 0.1410 sec
[61/200][25/32] 	Loss_D: -0.0840 | Loss_G: -0.4860 | Iteration Time: 0.1424 sec
Training Progress:  31%|███       | 62/200 [05:57<12:47,  5.56s/it]
[61/200][30/32] 	Loss_D: -0.0744 | Loss_G: -0.4259 | Iteration Time: 0.1490 sec
[62/200][0/32] 	Loss_D: -0.1000 | Loss_G: -0.4543 | Iteration Time: 0.1445 sec
[62/200][5/32] 	Loss_D: -0.0910 | Loss_G: -0.4315 | Iteration Time: 0.1430 sec
[62/200][10/32] 	Loss_D: -0.1048 | Loss_G: -0.4770 | Iteration Time: 0.1450 sec
[62/200][15/32] 	Loss_D: -0.0805 | Loss_G: -0.4714 | Iteration Time: 0.1435 sec
Current scores at iteration 2000 | FID: 239.5519561767578 | IS: 2.228182554244995
No description has been provided for this image
[62/200][20/32] 	Loss_D: -0.0842 | Loss_G: -0.4169 | Iteration Time: 0.1375 sec
[62/200][25/32] 	Loss_D: -0.0734 | Loss_G: -0.4728 | Iteration Time: 0.1445 sec
Training Progress:  32%|███▏      | 63/200 [06:08<16:18,  7.14s/it]
[62/200][30/32] 	Loss_D: -0.0816 | Loss_G: -0.5041 | Iteration Time: 0.1426 sec
[63/200][0/32] 	Loss_D: -0.0586 | Loss_G: -0.4839 | Iteration Time: 0.1465 sec
[63/200][5/32] 	Loss_D: -0.0898 | Loss_G: -0.4233 | Iteration Time: 0.1475 sec
[63/200][10/32] 	Loss_D: -0.0875 | Loss_G: -0.4606 | Iteration Time: 0.1405 sec
[63/200][15/32] 	Loss_D: -0.0849 | Loss_G: -0.4209 | Iteration Time: 0.1415 sec
[63/200][20/32] 	Loss_D: -0.0792 | Loss_G: -0.4194 | Iteration Time: 0.1420 sec
[63/200][25/32] 	Loss_D: -0.0778 | Loss_G: -0.4836 | Iteration Time: 0.1415 sec
Training Progress:  32%|███▏      | 64/200 [06:13<15:06,  6.66s/it]
[63/200][30/32] 	Loss_D: -0.0824 | Loss_G: -0.4875 | Iteration Time: 0.1477 sec
[64/200][0/32] 	Loss_D: -0.0842 | Loss_G: -0.4681 | Iteration Time: 0.1435 sec
[64/200][5/32] 	Loss_D: -0.0938 | Loss_G: -0.4262 | Iteration Time: 0.1395 sec
[64/200][10/32] 	Loss_D: -0.0828 | Loss_G: -0.4720 | Iteration Time: 0.1430 sec
[64/200][15/32] 	Loss_D: -0.0751 | Loss_G: -0.4766 | Iteration Time: 0.1450 sec
[64/200][20/32] 	Loss_D: -0.0584 | Loss_G: -0.4979 | Iteration Time: 0.1475 sec
[64/200][25/32] 	Loss_D: -0.0742 | Loss_G: -0.4857 | Iteration Time: 0.1445 sec
Training Progress:  32%|███▎      | 65/200 [06:19<14:15,  6.34s/it]
[64/200][30/32] 	Loss_D: -0.0926 | Loss_G: -0.4238 | Iteration Time: 0.1455 sec
[65/200][0/32] 	Loss_D: -0.1058 | Loss_G: -0.4600 | Iteration Time: 0.1470 sec
[65/200][5/32] 	Loss_D: -0.0892 | Loss_G: -0.4822 | Iteration Time: 0.1436 sec
[65/200][10/32] 	Loss_D: -0.0805 | Loss_G: -0.4251 | Iteration Time: 0.1470 sec
[65/200][15/32] 	Loss_D: -0.0988 | Loss_G: -0.4892 | Iteration Time: 0.1425 sec
[65/200][20/32] 	Loss_D: -0.0837 | Loss_G: -0.4211 | Iteration Time: 0.1430 sec
[65/200][25/32] 	Loss_D: -0.0815 | Loss_G: -0.4697 | Iteration Time: 0.1415 sec
Training Progress:  33%|███▎      | 66/200 [06:25<13:37,  6.10s/it]
[65/200][30/32] 	Loss_D: -0.0646 | Loss_G: -0.4761 | Iteration Time: 0.1415 sec
[66/200][0/32] 	Loss_D: -0.0838 | Loss_G: -0.4771 | Iteration Time: 0.1590 sec
[66/200][5/32] 	Loss_D: -0.0832 | Loss_G: -0.4248 | Iteration Time: 0.1410 sec
[66/200][10/32] 	Loss_D: -0.0891 | Loss_G: -0.4285 | Iteration Time: 0.1430 sec
[66/200][15/32] 	Loss_D: -0.0868 | Loss_G: -0.4735 | Iteration Time: 0.1450 sec
[66/200][20/32] 	Loss_D: -0.0832 | Loss_G: -0.4263 | Iteration Time: 0.1445 sec
[66/200][25/32] 	Loss_D: -0.0980 | Loss_G: -0.4480 | Iteration Time: 0.1460 sec
Training Progress:  34%|███▎      | 67/200 [06:30<13:11,  5.95s/it]
[66/200][30/32] 	Loss_D: -0.0728 | Loss_G: -0.4710 | Iteration Time: 0.1450 sec
[67/200][0/32] 	Loss_D: -0.0772 | Loss_G: -0.4710 | Iteration Time: 0.1595 sec
[67/200][5/32] 	Loss_D: -0.1023 | Loss_G: -0.4562 | Iteration Time: 0.1450 sec
[67/200][10/32] 	Loss_D: -0.0757 | Loss_G: -0.4234 | Iteration Time: 0.1432 sec
[67/200][15/32] 	Loss_D: -0.0625 | Loss_G: -0.5013 | Iteration Time: 0.1405 sec
[67/200][20/32] 	Loss_D: -0.0805 | Loss_G: -0.4209 | Iteration Time: 0.1415 sec
[67/200][25/32] 	Loss_D: -0.0840 | Loss_G: -0.4814 | Iteration Time: 0.1453 sec
Training Progress:  34%|███▍      | 68/200 [06:36<12:48,  5.82s/it]
[67/200][30/32] 	Loss_D: -0.0825 | Loss_G: -0.4788 | Iteration Time: 0.1395 sec
[68/200][0/32] 	Loss_D: -0.0772 | Loss_G: -0.4824 | Iteration Time: 0.1505 sec
[68/200][5/32] 	Loss_D: -0.0670 | Loss_G: -0.4911 | Iteration Time: 0.1450 sec
[68/200][10/32] 	Loss_D: -0.0698 | Loss_G: -0.4768 | Iteration Time: 0.1440 sec
[68/200][15/32] 	Loss_D: -0.0772 | Loss_G: -0.4723 | Iteration Time: 0.1470 sec
[68/200][20/32] 	Loss_D: -0.0931 | Loss_G: -0.4496 | Iteration Time: 0.1430 sec
[68/200][25/32] 	Loss_D: -0.0856 | Loss_G: -0.4639 | Iteration Time: 0.1400 sec
Training Progress:  34%|███▍      | 69/200 [06:41<12:31,  5.74s/it]
[68/200][30/32] 	Loss_D: -0.0702 | Loss_G: -0.4818 | Iteration Time: 0.1410 sec
[69/200][0/32] 	Loss_D: -0.0788 | Loss_G: -0.4864 | Iteration Time: 0.1485 sec
[69/200][5/32] 	Loss_D: -0.0715 | Loss_G: -0.4777 | Iteration Time: 0.1440 sec
[69/200][10/32] 	Loss_D: -0.0778 | Loss_G: -0.4297 | Iteration Time: 0.1450 sec
[69/200][15/32] 	Loss_D: -0.0931 | Loss_G: -0.4497 | Iteration Time: 0.1430 sec
[69/200][20/32] 	Loss_D: -0.0725 | Loss_G: -0.4266 | Iteration Time: 0.1430 sec
[69/200][25/32] 	Loss_D: -0.0891 | Loss_G: -0.4713 | Iteration Time: 0.1430 sec
Training Progress:  35%|███▌      | 70/200 [06:47<12:19,  5.69s/it]
[69/200][30/32] 	Loss_D: -0.0684 | Loss_G: -0.4964 | Iteration Time: 0.1475 sec
[70/200][0/32] 	Loss_D: -0.0648 | Loss_G: -0.4192 | Iteration Time: 0.1465 sec
[70/200][5/32] 	Loss_D: -0.0818 | Loss_G: -0.4752 | Iteration Time: 0.1421 sec
[70/200][10/32] 	Loss_D: -0.0948 | Loss_G: -0.4387 | Iteration Time: 0.1430 sec
[70/200][15/32] 	Loss_D: -0.0835 | Loss_G: -0.4811 | Iteration Time: 0.1435 sec
[70/200][20/32] 	Loss_D: -0.0592 | Loss_G: -0.4702 | Iteration Time: 0.1520 sec
[70/200][25/32] 	Loss_D: -0.1028 | Loss_G: -0.4315 | Iteration Time: 0.1440 sec
Training Progress:  36%|███▌      | 71/200 [06:52<12:10,  5.66s/it]
[70/200][30/32] 	Loss_D: -0.0773 | Loss_G: -0.4873 | Iteration Time: 0.1480 sec
[71/200][0/32] 	Loss_D: -0.0860 | Loss_G: -0.4227 | Iteration Time: 0.1470 sec
[71/200][5/32] 	Loss_D: -0.0860 | Loss_G: -0.4361 | Iteration Time: 0.1450 sec
[71/200][10/32] 	Loss_D: -0.0842 | Loss_G: -0.5079 | Iteration Time: 0.1410 sec
[71/200][15/32] 	Loss_D: -0.0926 | Loss_G: -0.4769 | Iteration Time: 0.1450 sec
[71/200][20/32] 	Loss_D: -0.0659 | Loss_G: -0.4904 | Iteration Time: 0.1490 sec
[71/200][25/32] 	Loss_D: -0.0747 | Loss_G: -0.4355 | Iteration Time: 0.1432 sec
Training Progress:  36%|███▌      | 72/200 [06:58<12:01,  5.64s/it]
[71/200][30/32] 	Loss_D: -0.0808 | Loss_G: -0.4259 | Iteration Time: 0.1450 sec
[72/200][0/32] 	Loss_D: -0.1029 | Loss_G: -0.4514 | Iteration Time: 0.1600 sec
[72/200][5/32] 	Loss_D: -0.0702 | Loss_G: -0.4933 | Iteration Time: 0.1415 sec
[72/200][10/32] 	Loss_D: -0.0929 | Loss_G: -0.4765 | Iteration Time: 0.1460 sec
[72/200][15/32] 	Loss_D: -0.0365 | Loss_G: -0.4552 | Iteration Time: 0.1395 sec
[72/200][20/32] 	Loss_D: -0.0892 | Loss_G: -0.4902 | Iteration Time: 0.1445 sec
[72/200][25/32] 	Loss_D: -0.0818 | Loss_G: -0.4865 | Iteration Time: 0.1435 sec
Training Progress:  36%|███▋      | 73/200 [07:04<11:52,  5.61s/it]
[72/200][30/32] 	Loss_D: -0.0730 | Loss_G: -0.4307 | Iteration Time: 0.1420 sec
[73/200][0/32] 	Loss_D: -0.0801 | Loss_G: -0.4914 | Iteration Time: 0.1575 sec
[73/200][5/32] 	Loss_D: -0.0823 | Loss_G: -0.4994 | Iteration Time: 0.1444 sec
[73/200][10/32] 	Loss_D: -0.0700 | Loss_G: -0.4889 | Iteration Time: 0.1440 sec
[73/200][15/32] 	Loss_D: -0.0664 | Loss_G: -0.4992 | Iteration Time: 0.1435 sec
[73/200][20/32] 	Loss_D: -0.0729 | Loss_G: -0.4982 | Iteration Time: 0.1435 sec
[73/200][25/32] 	Loss_D: -0.0811 | Loss_G: -0.4838 | Iteration Time: 0.1470 sec
Training Progress:  37%|███▋      | 74/200 [07:09<11:44,  5.59s/it]
[73/200][30/32] 	Loss_D: -0.0838 | Loss_G: -0.4974 | Iteration Time: 0.1435 sec
[74/200][0/32] 	Loss_D: -0.0842 | Loss_G: -0.4859 | Iteration Time: 0.1510 sec
[74/200][5/32] 	Loss_D: -0.0853 | Loss_G: -0.4566 | Iteration Time: 0.1415 sec
[74/200][10/32] 	Loss_D: -0.0669 | Loss_G: -0.4261 | Iteration Time: 0.1420 sec
[74/200][15/32] 	Loss_D: -0.0968 | Loss_G: -0.4318 | Iteration Time: 0.1430 sec
[74/200][20/32] 	Loss_D: -0.0711 | Loss_G: -0.4220 | Iteration Time: 0.1425 sec
[74/200][25/32] 	Loss_D: -0.0688 | Loss_G: -0.4880 | Iteration Time: 0.1440 sec
Training Progress:  38%|███▊      | 75/200 [07:15<11:37,  5.58s/it]
[74/200][30/32] 	Loss_D: -0.0960 | Loss_G: -0.4441 | Iteration Time: 0.1410 sec
[75/200][0/32] 	Loss_D: -0.0885 | Loss_G: -0.4784 | Iteration Time: 0.1515 sec
[75/200][5/32] 	Loss_D: -0.0872 | Loss_G: -0.4802 | Iteration Time: 0.1445 sec
[75/200][10/32] 	Loss_D: -0.0724 | Loss_G: -0.4357 | Iteration Time: 0.1435 sec
[75/200][15/32] 	Loss_D: -0.0875 | Loss_G: -0.4149 | Iteration Time: 0.1420 sec
[75/200][20/32] 	Loss_D: -0.0747 | Loss_G: -0.4884 | Iteration Time: 0.1452 sec
[75/200][25/32] 	Loss_D: -0.0808 | Loss_G: -0.4647 | Iteration Time: 0.1420 sec
Training Progress:  38%|███▊      | 76/200 [07:20<11:30,  5.57s/it]
[75/200][30/32] 	Loss_D: -0.0695 | Loss_G: -0.4216 | Iteration Time: 0.1445 sec
[76/200][0/32] 	Loss_D: -0.0849 | Loss_G: -0.4782 | Iteration Time: 0.1600 sec
[76/200][5/32] 	Loss_D: -0.0708 | Loss_G: -0.4375 | Iteration Time: 0.1435 sec
[76/200][10/32] 	Loss_D: -0.0755 | Loss_G: -0.4705 | Iteration Time: 0.1438 sec
[76/200][15/32] 	Loss_D: -0.0750 | Loss_G: -0.4917 | Iteration Time: 0.1405 sec
[76/200][20/32] 	Loss_D: -0.0675 | Loss_G: -0.4829 | Iteration Time: 0.1430 sec
[76/200][25/32] 	Loss_D: -0.0792 | Loss_G: -0.4264 | Iteration Time: 0.1490 sec
Training Progress:  38%|███▊      | 77/200 [07:26<11:22,  5.55s/it]
[76/200][30/32] 	Loss_D: -0.0955 | Loss_G: -0.4523 | Iteration Time: 0.1420 sec
[77/200][0/32] 	Loss_D: -0.0656 | Loss_G: -0.4143 | Iteration Time: 0.1515 sec
[77/200][5/32] 	Loss_D: -0.0662 | Loss_G: -0.4791 | Iteration Time: 0.1435 sec
[77/200][10/32] 	Loss_D: -0.0836 | Loss_G: -0.4678 | Iteration Time: 0.1445 sec
[77/200][15/32] 	Loss_D: -0.0865 | Loss_G: -0.4803 | Iteration Time: 0.1480 sec
[77/200][20/32] 	Loss_D: -0.0862 | Loss_G: -0.4701 | Iteration Time: 0.1435 sec
[77/200][25/32] 	Loss_D: -0.0833 | Loss_G: -0.4259 | Iteration Time: 0.1426 sec
Training Progress:  39%|███▉      | 78/200 [07:31<11:17,  5.56s/it]
[77/200][30/32] 	Loss_D: -0.0750 | Loss_G: -0.4664 | Iteration Time: 0.1415 sec
[78/200][0/32] 	Loss_D: -0.0876 | Loss_G: -0.4486 | Iteration Time: 0.1455 sec
Current scores at iteration 2500 | FID: 207.3428955078125 | IS: 2.4028067588806152
No description has been provided for this image
[78/200][5/32] 	Loss_D: -0.1071 | Loss_G: -0.4344 | Iteration Time: 0.3388 sec
[78/200][10/32] 	Loss_D: -0.0676 | Loss_G: -0.4229 | Iteration Time: 0.1444 sec
[78/200][15/32] 	Loss_D: -0.0841 | Loss_G: -0.4249 | Iteration Time: 0.1430 sec
[78/200][20/32] 	Loss_D: -0.0659 | Loss_G: -0.4689 | Iteration Time: 0.1400 sec
[78/200][25/32] 	Loss_D: -0.0723 | Loss_G: -0.5320 | Iteration Time: 0.1425 sec
Training Progress:  40%|███▉      | 79/200 [07:42<14:22,  7.12s/it]
[78/200][30/32] 	Loss_D: -0.0858 | Loss_G: -0.4394 | Iteration Time: 0.1440 sec
[79/200][0/32] 	Loss_D: -0.0946 | Loss_G: -0.4687 | Iteration Time: 0.1485 sec
[79/200][5/32] 	Loss_D: -0.0817 | Loss_G: -0.4215 | Iteration Time: 0.1425 sec
[79/200][10/32] 	Loss_D: -0.0717 | Loss_G: -0.4828 | Iteration Time: 0.1435 sec
[79/200][15/32] 	Loss_D: -0.0743 | Loss_G: -0.4147 | Iteration Time: 0.1445 sec
[79/200][20/32] 	Loss_D: -0.0722 | Loss_G: -0.4849 | Iteration Time: 0.1445 sec
[79/200][25/32] 	Loss_D: -0.0652 | Loss_G: -0.4771 | Iteration Time: 0.1450 sec
Training Progress:  40%|████      | 80/200 [07:48<13:18,  6.65s/it]
[79/200][30/32] 	Loss_D: -0.0928 | Loss_G: -0.4856 | Iteration Time: 0.1468 sec
[80/200][0/32] 	Loss_D: -0.0873 | Loss_G: -0.4500 | Iteration Time: 0.1425 sec
[80/200][5/32] 	Loss_D: -0.0737 | Loss_G: -0.4963 | Iteration Time: 0.1439 sec
[80/200][10/32] 	Loss_D: -0.0978 | Loss_G: -0.4339 | Iteration Time: 0.1420 sec
[80/200][15/32] 	Loss_D: -0.0764 | Loss_G: -0.4859 | Iteration Time: 0.1412 sec
[80/200][20/32] 	Loss_D: -0.0854 | Loss_G: -0.5065 | Iteration Time: 0.1425 sec
[80/200][25/32] 	Loss_D: -0.0695 | Loss_G: -0.4239 | Iteration Time: 0.1430 sec
Training Progress:  40%|████      | 81/200 [07:53<12:31,  6.32s/it]
[80/200][30/32] 	Loss_D: -0.0745 | Loss_G: -0.4701 | Iteration Time: 0.1407 sec
[81/200][0/32] 	Loss_D: -0.0874 | Loss_G: -0.4242 | Iteration Time: 0.1535 sec
[81/200][5/32] 	Loss_D: -0.0870 | Loss_G: -0.4481 | Iteration Time: 0.1400 sec
[81/200][10/32] 	Loss_D: -0.0737 | Loss_G: -0.4218 | Iteration Time: 0.1440 sec
[81/200][15/32] 	Loss_D: -0.0747 | Loss_G: -0.4829 | Iteration Time: 0.1445 sec
[81/200][20/32] 	Loss_D: -0.0795 | Loss_G: -0.4866 | Iteration Time: 0.1460 sec
[81/200][25/32] 	Loss_D: -0.0679 | Loss_G: -0.4821 | Iteration Time: 0.1515 sec
Training Progress:  41%|████      | 82/200 [07:59<11:58,  6.09s/it]
[81/200][30/32] 	Loss_D: -0.0894 | Loss_G: -0.4704 | Iteration Time: 0.1420 sec
[82/200][0/32] 	Loss_D: -0.0966 | Loss_G: -0.4251 | Iteration Time: 0.1505 sec
[82/200][5/32] 	Loss_D: -0.0736 | Loss_G: -0.4875 | Iteration Time: 0.1450 sec
[82/200][10/32] 	Loss_D: -0.0809 | Loss_G: -0.4851 | Iteration Time: 0.1403 sec
[82/200][15/32] 	Loss_D: -0.0796 | Loss_G: -0.4209 | Iteration Time: 0.1445 sec
[82/200][20/32] 	Loss_D: -0.0737 | Loss_G: -0.4770 | Iteration Time: 0.1410 sec
[82/200][25/32] 	Loss_D: -0.0899 | Loss_G: -0.4275 | Iteration Time: 0.1425 sec
Training Progress:  42%|████▏     | 83/200 [08:04<11:33,  5.93s/it]
[82/200][30/32] 	Loss_D: -0.0608 | Loss_G: -0.4217 | Iteration Time: 0.1445 sec
[83/200][0/32] 	Loss_D: -0.0838 | Loss_G: -0.5023 | Iteration Time: 0.1495 sec
[83/200][5/32] 	Loss_D: -0.0572 | Loss_G: -0.4986 | Iteration Time: 0.1470 sec
[83/200][10/32] 	Loss_D: -0.0866 | Loss_G: -0.4302 | Iteration Time: 0.1425 sec
[83/200][15/32] 	Loss_D: -0.0905 | Loss_G: -0.4750 | Iteration Time: 0.1445 sec
[83/200][20/32] 	Loss_D: -0.0864 | Loss_G: -0.4741 | Iteration Time: 0.1450 sec
[83/200][25/32] 	Loss_D: -0.0893 | Loss_G: -0.4382 | Iteration Time: 0.1460 sec
Training Progress:  42%|████▏     | 84/200 [08:10<11:14,  5.82s/it]
[83/200][30/32] 	Loss_D: -0.0800 | Loss_G: -0.4255 | Iteration Time: 0.1480 sec
[84/200][0/32] 	Loss_D: -0.0581 | Loss_G: -0.4261 | Iteration Time: 0.1440 sec
[84/200][5/32] 	Loss_D: -0.0831 | Loss_G: -0.4848 | Iteration Time: 0.1429 sec
[84/200][10/32] 	Loss_D: -0.0916 | Loss_G: -0.4790 | Iteration Time: 0.1410 sec
[84/200][15/32] 	Loss_D: -0.0729 | Loss_G: -0.4887 | Iteration Time: 0.1410 sec
[84/200][20/32] 	Loss_D: -0.0788 | Loss_G: -0.4305 | Iteration Time: 0.1455 sec
[84/200][25/32] 	Loss_D: -0.0959 | Loss_G: -0.4600 | Iteration Time: 0.1419 sec
Training Progress:  42%|████▎     | 85/200 [08:15<10:59,  5.73s/it]
[84/200][30/32] 	Loss_D: -0.0775 | Loss_G: -0.4608 | Iteration Time: 0.1442 sec
[85/200][0/32] 	Loss_D: -0.0875 | Loss_G: -0.4896 | Iteration Time: 0.1580 sec
[85/200][5/32] 	Loss_D: -0.0837 | Loss_G: -0.4899 | Iteration Time: 0.1435 sec
[85/200][10/32] 	Loss_D: -0.0827 | Loss_G: -0.4890 | Iteration Time: 0.1435 sec
[85/200][15/32] 	Loss_D: -0.0780 | Loss_G: -0.4245 | Iteration Time: 0.1430 sec
[85/200][20/32] 	Loss_D: -0.1037 | Loss_G: -0.4402 | Iteration Time: 0.1425 sec
[85/200][25/32] 	Loss_D: -0.0706 | Loss_G: -0.4804 | Iteration Time: 0.1475 sec
Training Progress:  43%|████▎     | 86/200 [08:21<10:46,  5.67s/it]
[85/200][30/32] 	Loss_D: -0.0686 | Loss_G: -0.4228 | Iteration Time: 0.1400 sec
[86/200][0/32] 	Loss_D: -0.0896 | Loss_G: -0.4499 | Iteration Time: 0.1495 sec
[86/200][5/32] 	Loss_D: -0.0953 | Loss_G: -0.4463 | Iteration Time: 0.1435 sec
[86/200][10/32] 	Loss_D: -0.0762 | Loss_G: -0.4394 | Iteration Time: 0.1405 sec
[86/200][15/32] 	Loss_D: -0.0832 | Loss_G: -0.4732 | Iteration Time: 0.1421 sec
[86/200][20/32] 	Loss_D: -0.0923 | Loss_G: -0.4605 | Iteration Time: 0.1397 sec
[86/200][25/32] 	Loss_D: -0.0662 | Loss_G: -0.4924 | Iteration Time: 0.1420 sec
Training Progress:  44%|████▎     | 87/200 [08:26<10:35,  5.63s/it]
[86/200][30/32] 	Loss_D: -0.0529 | Loss_G: -0.4790 | Iteration Time: 0.1460 sec
[87/200][0/32] 	Loss_D: -0.0707 | Loss_G: -0.4271 | Iteration Time: 0.1445 sec
[87/200][5/32] 	Loss_D: -0.0824 | Loss_G: -0.4811 | Iteration Time: 0.1415 sec
[87/200][10/32] 	Loss_D: -0.0745 | Loss_G: -0.4205 | Iteration Time: 0.1405 sec
[87/200][15/32] 	Loss_D: -0.0900 | Loss_G: -0.4684 | Iteration Time: 0.1435 sec
[87/200][20/32] 	Loss_D: -0.0840 | Loss_G: -0.4818 | Iteration Time: 0.1450 sec
[87/200][25/32] 	Loss_D: -0.0441 | Loss_G: -0.4720 | Iteration Time: 0.1405 sec
Training Progress:  44%|████▍     | 88/200 [08:32<10:27,  5.60s/it]
[87/200][30/32] 	Loss_D: -0.0885 | Loss_G: -0.4395 | Iteration Time: 0.1445 sec
[88/200][0/32] 	Loss_D: -0.0688 | Loss_G: -0.4274 | Iteration Time: 0.1628 sec
[88/200][5/32] 	Loss_D: -0.0622 | Loss_G: -0.4786 | Iteration Time: 0.1440 sec
[88/200][10/32] 	Loss_D: -0.0895 | Loss_G: -0.4383 | Iteration Time: 0.1414 sec
[88/200][15/32] 	Loss_D: -0.0699 | Loss_G: -0.4866 | Iteration Time: 0.1435 sec
[88/200][20/32] 	Loss_D: -0.0849 | Loss_G: -0.4905 | Iteration Time: 0.1425 sec
[88/200][25/32] 	Loss_D: -0.0782 | Loss_G: -0.4251 | Iteration Time: 0.1477 sec
Training Progress:  44%|████▍     | 89/200 [08:38<10:20,  5.59s/it]
[88/200][30/32] 	Loss_D: -0.0443 | Loss_G: -0.4832 | Iteration Time: 0.1470 sec
[89/200][0/32] 	Loss_D: -0.0789 | Loss_G: -0.4262 | Iteration Time: 0.1595 sec
[89/200][5/32] 	Loss_D: -0.0950 | Loss_G: -0.4376 | Iteration Time: 0.1440 sec
[89/200][10/32] 	Loss_D: -0.0578 | Loss_G: -0.4777 | Iteration Time: 0.1495 sec
[89/200][15/32] 	Loss_D: -0.0694 | Loss_G: -0.4864 | Iteration Time: 0.1510 sec
[89/200][20/32] 	Loss_D: -0.0596 | Loss_G: -0.4247 | Iteration Time: 0.1490 sec
[89/200][25/32] 	Loss_D: -0.0834 | Loss_G: -0.4366 | Iteration Time: 0.1525 sec
Training Progress:  45%|████▌     | 90/200 [08:43<10:21,  5.65s/it]
[89/200][30/32] 	Loss_D: -0.0728 | Loss_G: -0.4295 | Iteration Time: 0.1505 sec
[90/200][0/32] 	Loss_D: -0.0967 | Loss_G: -0.4567 | Iteration Time: 0.1635 sec
[90/200][5/32] 	Loss_D: -0.0843 | Loss_G: -0.4260 | Iteration Time: 0.1515 sec
[90/200][10/32] 	Loss_D: -0.0806 | Loss_G: -0.4197 | Iteration Time: 0.1495 sec
[90/200][15/32] 	Loss_D: -0.0852 | Loss_G: -0.4407 | Iteration Time: 0.1465 sec
[90/200][20/32] 	Loss_D: -0.0790 | Loss_G: -0.4215 | Iteration Time: 0.1520 sec
[90/200][25/32] 	Loss_D: -0.0818 | Loss_G: -0.4264 | Iteration Time: 0.1485 sec
Training Progress:  46%|████▌     | 91/200 [08:49<10:20,  5.69s/it]
[90/200][30/32] 	Loss_D: -0.0795 | Loss_G: -0.4638 | Iteration Time: 0.1360 sec
[91/200][0/32] 	Loss_D: -0.0763 | Loss_G: -0.4908 | Iteration Time: 0.0955 sec
[91/200][5/32] 	Loss_D: -0.0847 | Loss_G: -0.5072 | Iteration Time: 0.0960 sec
[91/200][10/32] 	Loss_D: -0.1006 | Loss_G: -0.4248 | Iteration Time: 0.1130 sec
[91/200][15/32] 	Loss_D: -0.0763 | Loss_G: -0.4290 | Iteration Time: 0.1400 sec
[91/200][20/32] 	Loss_D: -0.0888 | Loss_G: -0.4377 | Iteration Time: 0.1500 sec
[91/200][25/32] 	Loss_D: -0.0776 | Loss_G: -0.4733 | Iteration Time: 0.1530 sec
Training Progress:  46%|████▌     | 92/200 [08:54<09:56,  5.52s/it]
[91/200][30/32] 	Loss_D: -0.0755 | Loss_G: -0.4866 | Iteration Time: 0.1485 sec
[92/200][0/32] 	Loss_D: -0.1008 | Loss_G: -0.4514 | Iteration Time: 0.1590 sec
[92/200][5/32] 	Loss_D: -0.0802 | Loss_G: -0.4720 | Iteration Time: 0.1500 sec
[92/200][10/32] 	Loss_D: -0.0695 | Loss_G: -0.4973 | Iteration Time: 0.1445 sec
[92/200][15/32] 	Loss_D: -0.0801 | Loss_G: -0.4400 | Iteration Time: 0.1455 sec
[92/200][20/32] 	Loss_D: -0.0791 | Loss_G: -0.4809 | Iteration Time: 0.1445 sec
[92/200][25/32] 	Loss_D: -0.0908 | Loss_G: -0.4368 | Iteration Time: 0.1450 sec
Training Progress:  46%|████▋     | 93/200 [09:00<09:53,  5.55s/it]
[92/200][30/32] 	Loss_D: -0.0909 | Loss_G: -0.4625 | Iteration Time: 0.1440 sec
[93/200][0/32] 	Loss_D: -0.0957 | Loss_G: -0.4287 | Iteration Time: 0.1455 sec
[93/200][5/32] 	Loss_D: -0.0887 | Loss_G: -0.4253 | Iteration Time: 0.1505 sec
[93/200][10/32] 	Loss_D: -0.0767 | Loss_G: -0.4246 | Iteration Time: 0.1430 sec
[93/200][15/32] 	Loss_D: -0.0725 | Loss_G: -0.4218 | Iteration Time: 0.1455 sec
[93/200][20/32] 	Loss_D: -0.0800 | Loss_G: -0.4830 | Iteration Time: 0.1415 sec
Current scores at iteration 3000 | FID: 195.464111328125 | IS: 2.2515273094177246
No description has been provided for this image
[93/200][25/32] 	Loss_D: -0.0888 | Loss_G: -0.4783 | Iteration Time: 0.4081 sec
Training Progress:  47%|████▋     | 94/200 [09:11<12:46,  7.24s/it]
[93/200][30/32] 	Loss_D: -0.0681 | Loss_G: -0.4252 | Iteration Time: 0.1420 sec
[94/200][0/32] 	Loss_D: -0.0821 | Loss_G: -0.4579 | Iteration Time: 0.1456 sec
[94/200][5/32] 	Loss_D: -0.0788 | Loss_G: -0.4762 | Iteration Time: 0.1435 sec
[94/200][10/32] 	Loss_D: -0.0731 | Loss_G: -0.4831 | Iteration Time: 0.1445 sec
[94/200][15/32] 	Loss_D: -0.0826 | Loss_G: -0.4479 | Iteration Time: 0.1440 sec
[94/200][20/32] 	Loss_D: -0.0820 | Loss_G: -0.4290 | Iteration Time: 0.1405 sec
[94/200][25/32] 	Loss_D: -0.0796 | Loss_G: -0.4865 | Iteration Time: 0.1470 sec
Training Progress:  48%|████▊     | 95/200 [09:17<11:50,  6.77s/it]
[94/200][30/32] 	Loss_D: -0.0765 | Loss_G: -0.4276 | Iteration Time: 0.1435 sec
[95/200][0/32] 	Loss_D: -0.0836 | Loss_G: -0.4705 | Iteration Time: 0.1490 sec
[95/200][5/32] 	Loss_D: -0.0863 | Loss_G: -0.4770 | Iteration Time: 0.1420 sec
[95/200][10/32] 	Loss_D: -0.0781 | Loss_G: -0.4323 | Iteration Time: 0.1420 sec
[95/200][15/32] 	Loss_D: -0.0899 | Loss_G: -0.4336 | Iteration Time: 0.1405 sec
[95/200][20/32] 	Loss_D: -0.0596 | Loss_G: -0.4726 | Iteration Time: 0.1410 sec
[95/200][25/32] 	Loss_D: -0.0771 | Loss_G: -0.4280 | Iteration Time: 0.1390 sec
Training Progress:  48%|████▊     | 96/200 [09:22<11:02,  6.37s/it]
[95/200][30/32] 	Loss_D: -0.0617 | Loss_G: -0.4928 | Iteration Time: 0.1410 sec
[96/200][0/32] 	Loss_D: -0.0682 | Loss_G: -0.4807 | Iteration Time: 0.1565 sec
[96/200][5/32] 	Loss_D: -0.0758 | Loss_G: -0.4295 | Iteration Time: 0.1400 sec
[96/200][10/32] 	Loss_D: -0.0800 | Loss_G: -0.4343 | Iteration Time: 0.1400 sec
[96/200][15/32] 	Loss_D: -0.0780 | Loss_G: -0.4251 | Iteration Time: 0.1410 sec
[96/200][20/32] 	Loss_D: -0.0666 | Loss_G: -0.4270 | Iteration Time: 0.1425 sec
[96/200][25/32] 	Loss_D: -0.0684 | Loss_G: -0.4803 | Iteration Time: 0.1425 sec
Training Progress:  48%|████▊     | 97/200 [09:28<10:28,  6.10s/it]
[96/200][30/32] 	Loss_D: -0.0769 | Loss_G: -0.4791 | Iteration Time: 0.1425 sec
[97/200][0/32] 	Loss_D: -0.0895 | Loss_G: -0.4277 | Iteration Time: 0.1480 sec
[97/200][5/32] 	Loss_D: -0.0793 | Loss_G: -0.4805 | Iteration Time: 0.1440 sec
[97/200][10/32] 	Loss_D: -0.0770 | Loss_G: -0.4242 | Iteration Time: 0.1395 sec
[97/200][15/32] 	Loss_D: -0.0991 | Loss_G: -0.4347 | Iteration Time: 0.1400 sec
[97/200][20/32] 	Loss_D: -0.0574 | Loss_G: -0.4895 | Iteration Time: 0.1415 sec
[97/200][25/32] 	Loss_D: -0.0748 | Loss_G: -0.4274 | Iteration Time: 0.1435 sec
Training Progress:  49%|████▉     | 98/200 [09:33<10:02,  5.91s/it]
[97/200][30/32] 	Loss_D: -0.0836 | Loss_G: -0.4818 | Iteration Time: 0.1395 sec
[98/200][0/32] 	Loss_D: -0.0817 | Loss_G: -0.4471 | Iteration Time: 0.1480 sec
[98/200][5/32] 	Loss_D: -0.0962 | Loss_G: -0.4845 | Iteration Time: 0.1405 sec
[98/200][10/32] 	Loss_D: -0.0852 | Loss_G: -0.4750 | Iteration Time: 0.1380 sec
[98/200][15/32] 	Loss_D: -0.0855 | Loss_G: -0.4300 | Iteration Time: 0.1435 sec
[98/200][20/32] 	Loss_D: -0.0906 | Loss_G: -0.4547 | Iteration Time: 0.1480 sec
[98/200][25/32] 	Loss_D: -0.0720 | Loss_G: -0.4196 | Iteration Time: 0.1410 sec
Training Progress:  50%|████▉     | 99/200 [09:39<09:43,  5.77s/it]
[98/200][30/32] 	Loss_D: -0.0834 | Loss_G: -0.4315 | Iteration Time: 0.1405 sec
[99/200][0/32] 	Loss_D: -0.0771 | Loss_G: -0.4917 | Iteration Time: 0.1535 sec
[99/200][5/32] 	Loss_D: -0.0849 | Loss_G: -0.4224 | Iteration Time: 0.1390 sec
[99/200][10/32] 	Loss_D: -0.0813 | Loss_G: -0.4322 | Iteration Time: 0.1385 sec
[99/200][15/32] 	Loss_D: -0.0693 | Loss_G: -0.4270 | Iteration Time: 0.1435 sec
[99/200][20/32] 	Loss_D: -0.0765 | Loss_G: -0.4924 | Iteration Time: 0.1405 sec
[99/200][25/32] 	Loss_D: -0.0635 | Loss_G: -0.4812 | Iteration Time: 0.1400 sec
Training Progress:  50%|█████     | 100/200 [09:44<09:26,  5.66s/it]
[99/200][30/32] 	Loss_D: -0.0940 | Loss_G: -0.4551 | Iteration Time: 0.1425 sec
[100/200][0/32] 	Loss_D: -0.0838 | Loss_G: -0.4813 | Iteration Time: 0.1550 sec
[100/200][5/32] 	Loss_D: -0.0729 | Loss_G: -0.4225 | Iteration Time: 0.1432 sec
[100/200][10/32] 	Loss_D: -0.0749 | Loss_G: -0.4343 | Iteration Time: 0.1445 sec
[100/200][15/32] 	Loss_D: -0.0851 | Loss_G: -0.4621 | Iteration Time: 0.1420 sec
[100/200][20/32] 	Loss_D: -0.0662 | Loss_G: -0.4263 | Iteration Time: 0.1400 sec
[100/200][25/32] 	Loss_D: -0.0816 | Loss_G: -0.4912 | Iteration Time: 0.1410 sec
Training Progress:  50%|█████     | 101/200 [09:49<09:15,  5.61s/it]
[100/200][30/32] 	Loss_D: -0.0794 | Loss_G: -0.4849 | Iteration Time: 0.1395 sec
[101/200][0/32] 	Loss_D: -0.0797 | Loss_G: -0.4764 | Iteration Time: 0.1465 sec
[101/200][5/32] 	Loss_D: -0.0650 | Loss_G: -0.4293 | Iteration Time: 0.1410 sec
[101/200][10/32] 	Loss_D: -0.0828 | Loss_G: -0.4328 | Iteration Time: 0.1425 sec
[101/200][15/32] 	Loss_D: -0.0727 | Loss_G: -0.4816 | Iteration Time: 0.1390 sec
[101/200][20/32] 	Loss_D: -0.0854 | Loss_G: -0.4912 | Iteration Time: 0.1440 sec
[101/200][25/32] 	Loss_D: -0.0755 | Loss_G: -0.4754 | Iteration Time: 0.1425 sec
Training Progress:  51%|█████     | 102/200 [09:55<09:05,  5.57s/it]
[101/200][30/32] 	Loss_D: -0.0813 | Loss_G: -0.4631 | Iteration Time: 0.1425 sec
[102/200][0/32] 	Loss_D: -0.0783 | Loss_G: -0.4366 | Iteration Time: 0.1505 sec
[102/200][5/32] 	Loss_D: -0.0752 | Loss_G: -0.4995 | Iteration Time: 0.1425 sec
[102/200][10/32] 	Loss_D: -0.0991 | Loss_G: -0.4269 | Iteration Time: 0.1440 sec
[102/200][15/32] 	Loss_D: -0.0673 | Loss_G: -0.4779 | Iteration Time: 0.1445 sec
[102/200][20/32] 	Loss_D: -0.0870 | Loss_G: -0.4684 | Iteration Time: 0.1410 sec
[102/200][25/32] 	Loss_D: -0.0863 | Loss_G: -0.4379 | Iteration Time: 0.1430 sec
Training Progress:  52%|█████▏    | 103/200 [10:00<08:57,  5.54s/it]
[102/200][30/32] 	Loss_D: -0.0807 | Loss_G: -0.4706 | Iteration Time: 0.1455 sec
[103/200][0/32] 	Loss_D: -0.0596 | Loss_G: -0.4855 | Iteration Time: 0.1465 sec
[103/200][5/32] 	Loss_D: -0.0716 | Loss_G: -0.4757 | Iteration Time: 0.1435 sec
[103/200][10/32] 	Loss_D: -0.0763 | Loss_G: -0.4269 | Iteration Time: 0.1485 sec
[103/200][15/32] 	Loss_D: -0.0857 | Loss_G: -0.4242 | Iteration Time: 0.1425 sec
[103/200][20/32] 	Loss_D: -0.0797 | Loss_G: -0.4760 | Iteration Time: 0.1440 sec
[103/200][25/32] 	Loss_D: -0.0693 | Loss_G: -0.5097 | Iteration Time: 0.1390 sec
Training Progress:  52%|█████▏    | 104/200 [10:06<08:50,  5.53s/it]
[103/200][30/32] 	Loss_D: -0.0720 | Loss_G: -0.4719 | Iteration Time: 0.1435 sec
[104/200][0/32] 	Loss_D: -0.0904 | Loss_G: -0.4301 | Iteration Time: 0.1465 sec
[104/200][5/32] 	Loss_D: -0.0983 | Loss_G: -0.4477 | Iteration Time: 0.1420 sec
[104/200][10/32] 	Loss_D: -0.0798 | Loss_G: -0.4239 | Iteration Time: 0.1420 sec
[104/200][15/32] 	Loss_D: -0.0686 | Loss_G: -0.4884 | Iteration Time: 0.1430 sec
[104/200][20/32] 	Loss_D: -0.0938 | Loss_G: -0.4509 | Iteration Time: 0.1420 sec
[104/200][25/32] 	Loss_D: -0.0785 | Loss_G: -0.4275 | Iteration Time: 0.1435 sec
Training Progress:  52%|█████▎    | 105/200 [10:11<08:44,  5.52s/it]
[104/200][30/32] 	Loss_D: -0.0752 | Loss_G: -0.4833 | Iteration Time: 0.1455 sec
[105/200][0/32] 	Loss_D: -0.0790 | Loss_G: -0.4713 | Iteration Time: 0.1448 sec
[105/200][5/32] 	Loss_D: -0.0791 | Loss_G: -0.5030 | Iteration Time: 0.1425 sec
[105/200][10/32] 	Loss_D: -0.0839 | Loss_G: -0.4812 | Iteration Time: 0.1450 sec
[105/200][15/32] 	Loss_D: -0.0752 | Loss_G: -0.4307 | Iteration Time: 0.1410 sec
[105/200][20/32] 	Loss_D: -0.0697 | Loss_G: -0.4656 | Iteration Time: 0.1400 sec
[105/200][25/32] 	Loss_D: -0.0880 | Loss_G: -0.4425 | Iteration Time: 0.1425 sec
Training Progress:  53%|█████▎    | 106/200 [10:17<08:36,  5.50s/it]
[105/200][30/32] 	Loss_D: -0.0824 | Loss_G: -0.4649 | Iteration Time: 0.1395 sec
[106/200][0/32] 	Loss_D: -0.0689 | Loss_G: -0.4989 | Iteration Time: 0.1455 sec
[106/200][5/32] 	Loss_D: -0.0831 | Loss_G: -0.4701 | Iteration Time: 0.1445 sec
[106/200][10/32] 	Loss_D: -0.0825 | Loss_G: -0.4854 | Iteration Time: 0.1390 sec
[106/200][15/32] 	Loss_D: -0.0704 | Loss_G: -0.4733 | Iteration Time: 0.1430 sec
[106/200][20/32] 	Loss_D: -0.0804 | Loss_G: -0.4708 | Iteration Time: 0.1460 sec
[106/200][25/32] 	Loss_D: -0.0811 | Loss_G: -0.4611 | Iteration Time: 0.1415 sec
Training Progress:  54%|█████▎    | 107/200 [10:22<08:30,  5.49s/it]
[106/200][30/32] 	Loss_D: -0.0804 | Loss_G: -0.4625 | Iteration Time: 0.1410 sec
[107/200][0/32] 	Loss_D: -0.0643 | Loss_G: -0.4708 | Iteration Time: 0.1556 sec
[107/200][5/32] 	Loss_D: -0.0738 | Loss_G: -0.4349 | Iteration Time: 0.1425 sec
[107/200][10/32] 	Loss_D: -0.0950 | Loss_G: -0.4563 | Iteration Time: 0.1430 sec
[107/200][15/32] 	Loss_D: -0.0855 | Loss_G: -0.4343 | Iteration Time: 0.1420 sec
[107/200][20/32] 	Loss_D: -0.0790 | Loss_G: -0.4313 | Iteration Time: 0.1420 sec
[107/200][25/32] 	Loss_D: -0.0712 | Loss_G: -0.4706 | Iteration Time: 0.1455 sec
Training Progress:  54%|█████▍    | 108/200 [10:28<08:25,  5.49s/it]
[107/200][30/32] 	Loss_D: -0.0899 | Loss_G: -0.4254 | Iteration Time: 0.1400 sec
[108/200][0/32] 	Loss_D: -0.0970 | Loss_G: -0.4517 | Iteration Time: 0.1475 sec
[108/200][5/32] 	Loss_D: -0.0794 | Loss_G: -0.4307 | Iteration Time: 0.1440 sec
[108/200][10/32] 	Loss_D: -0.0854 | Loss_G: -0.4725 | Iteration Time: 0.1415 sec
[108/200][15/32] 	Loss_D: -0.0914 | Loss_G: -0.4530 | Iteration Time: 0.1415 sec
[108/200][20/32] 	Loss_D: -0.0747 | Loss_G: -0.4814 | Iteration Time: 0.1460 sec
[108/200][25/32] 	Loss_D: -0.0702 | Loss_G: -0.4824 | Iteration Time: 0.1425 sec
Training Progress:  55%|█████▍    | 109/200 [10:33<08:20,  5.50s/it]
[108/200][30/32] 	Loss_D: -0.0746 | Loss_G: -0.4304 | Iteration Time: 0.1446 sec
[109/200][0/32] 	Loss_D: -0.0799 | Loss_G: -0.4743 | Iteration Time: 0.1605 sec
[109/200][5/32] 	Loss_D: -0.0821 | Loss_G: -0.4207 | Iteration Time: 0.1410 sec
[109/200][10/32] 	Loss_D: -0.0996 | Loss_G: -0.4329 | Iteration Time: 0.1425 sec
Current scores at iteration 3500 | FID: 204.6544647216797 | IS: 2.431602954864502
No description has been provided for this image
[109/200][15/32] 	Loss_D: -0.0919 | Loss_G: -0.4413 | Iteration Time: 0.1385 sec
[109/200][20/32] 	Loss_D: -0.0910 | Loss_G: -0.4246 | Iteration Time: 0.1450 sec
[109/200][25/32] 	Loss_D: -0.0797 | Loss_G: -0.4323 | Iteration Time: 0.1435 sec
Training Progress:  55%|█████▌    | 110/200 [10:44<10:44,  7.16s/it]
[109/200][30/32] 	Loss_D: -0.0825 | Loss_G: -0.4264 | Iteration Time: 0.1450 sec
[110/200][0/32] 	Loss_D: -0.0917 | Loss_G: -0.4648 | Iteration Time: 0.1545 sec
[110/200][5/32] 	Loss_D: -0.0773 | Loss_G: -0.4858 | Iteration Time: 0.1440 sec
[110/200][10/32] 	Loss_D: -0.0710 | Loss_G: -0.4304 | Iteration Time: 0.1470 sec
[110/200][15/32] 	Loss_D: -0.0952 | Loss_G: -0.4327 | Iteration Time: 0.1435 sec
[110/200][20/32] 	Loss_D: -0.0755 | Loss_G: -0.4229 | Iteration Time: 0.1425 sec
[110/200][25/32] 	Loss_D: -0.0889 | Loss_G: -0.4491 | Iteration Time: 0.1445 sec
Training Progress:  56%|█████▌    | 111/200 [10:50<09:53,  6.67s/it]
[110/200][30/32] 	Loss_D: -0.0713 | Loss_G: -0.4712 | Iteration Time: 0.1445 sec
[111/200][0/32] 	Loss_D: -0.0853 | Loss_G: -0.4873 | Iteration Time: 0.1540 sec
[111/200][5/32] 	Loss_D: -0.0841 | Loss_G: -0.4299 | Iteration Time: 0.1410 sec
[111/200][10/32] 	Loss_D: -0.0591 | Loss_G: -0.4845 | Iteration Time: 0.1435 sec
[111/200][15/32] 	Loss_D: -0.0822 | Loss_G: -0.4942 | Iteration Time: 0.1480 sec
[111/200][20/32] 	Loss_D: -0.0952 | Loss_G: -0.4624 | Iteration Time: 0.1445 sec
[111/200][25/32] 	Loss_D: -0.0947 | Loss_G: -0.4663 | Iteration Time: 0.1450 sec
Training Progress:  56%|█████▌    | 112/200 [10:55<09:17,  6.33s/it]
[111/200][30/32] 	Loss_D: -0.0732 | Loss_G: -0.4229 | Iteration Time: 0.1435 sec
[112/200][0/32] 	Loss_D: -0.0905 | Loss_G: -0.4562 | Iteration Time: 0.1475 sec
[112/200][5/32] 	Loss_D: -0.0720 | Loss_G: -0.4939 | Iteration Time: 0.1440 sec
[112/200][10/32] 	Loss_D: -0.0710 | Loss_G: -0.4215 | Iteration Time: 0.1415 sec
[112/200][15/32] 	Loss_D: -0.0752 | Loss_G: -0.4772 | Iteration Time: 0.1455 sec
[112/200][20/32] 	Loss_D: -0.0661 | Loss_G: -0.4823 | Iteration Time: 0.1425 sec
[112/200][25/32] 	Loss_D: -0.0837 | Loss_G: -0.4363 | Iteration Time: 0.1415 sec
Training Progress:  56%|█████▋    | 113/200 [11:01<08:50,  6.09s/it]
[112/200][30/32] 	Loss_D: -0.0794 | Loss_G: -0.4813 | Iteration Time: 0.1444 sec
[113/200][0/32] 	Loss_D: -0.0831 | Loss_G: -0.4814 | Iteration Time: 0.1455 sec
[113/200][5/32] 	Loss_D: -0.0685 | Loss_G: -0.4756 | Iteration Time: 0.1415 sec
[113/200][10/32] 	Loss_D: -0.0637 | Loss_G: -0.4660 | Iteration Time: 0.1460 sec
[113/200][15/32] 	Loss_D: -0.0596 | Loss_G: -0.4847 | Iteration Time: 0.1420 sec
[113/200][20/32] 	Loss_D: -0.0711 | Loss_G: -0.4873 | Iteration Time: 0.1430 sec
[113/200][25/32] 	Loss_D: -0.0677 | Loss_G: -0.4909 | Iteration Time: 0.1440 sec
Training Progress:  57%|█████▋    | 114/200 [11:07<08:29,  5.93s/it]
[113/200][30/32] 	Loss_D: -0.0783 | Loss_G: -0.4258 | Iteration Time: 0.1440 sec
[114/200][0/32] 	Loss_D: -0.0738 | Loss_G: -0.4299 | Iteration Time: 0.1565 sec
[114/200][5/32] 	Loss_D: -0.0761 | Loss_G: -0.4836 | Iteration Time: 0.1445 sec
[114/200][10/32] 	Loss_D: -0.0775 | Loss_G: -0.4280 | Iteration Time: 0.1480 sec
[114/200][15/32] 	Loss_D: -0.0851 | Loss_G: -0.4788 | Iteration Time: 0.1440 sec
[114/200][20/32] 	Loss_D: -0.0737 | Loss_G: -0.4259 | Iteration Time: 0.1415 sec
[114/200][25/32] 	Loss_D: -0.0655 | Loss_G: -0.4817 | Iteration Time: 0.1455 sec
Training Progress:  57%|█████▊    | 115/200 [11:12<08:14,  5.81s/it]
[114/200][30/32] 	Loss_D: -0.0635 | Loss_G: -0.4843 | Iteration Time: 0.1420 sec
[115/200][0/32] 	Loss_D: -0.0685 | Loss_G: -0.4245 | Iteration Time: 0.1550 sec
[115/200][5/32] 	Loss_D: -0.0694 | Loss_G: -0.4775 | Iteration Time: 0.1430 sec
[115/200][10/32] 	Loss_D: -0.0765 | Loss_G: -0.4289 | Iteration Time: 0.1420 sec
[115/200][15/32] 	Loss_D: -0.0832 | Loss_G: -0.4845 | Iteration Time: 0.1445 sec
[115/200][20/32] 	Loss_D: -0.0759 | Loss_G: -0.4756 | Iteration Time: 0.1400 sec
[115/200][25/32] 	Loss_D: -0.0591 | Loss_G: -0.4778 | Iteration Time: 0.1445 sec
Training Progress:  58%|█████▊    | 116/200 [11:18<08:01,  5.73s/it]
[115/200][30/32] 	Loss_D: -0.0697 | Loss_G: -0.4237 | Iteration Time: 0.1450 sec
[116/200][0/32] 	Loss_D: -0.0798 | Loss_G: -0.4921 | Iteration Time: 0.1501 sec
[116/200][5/32] 	Loss_D: -0.0888 | Loss_G: -0.4382 | Iteration Time: 0.1470 sec
[116/200][10/32] 	Loss_D: -0.0634 | Loss_G: -0.4270 | Iteration Time: 0.1460 sec
[116/200][15/32] 	Loss_D: -0.0927 | Loss_G: -0.4244 | Iteration Time: 0.1460 sec
[116/200][20/32] 	Loss_D: -0.0831 | Loss_G: -0.4702 | Iteration Time: 0.1425 sec
[116/200][25/32] 	Loss_D: -0.0747 | Loss_G: -0.4271 | Iteration Time: 0.1405 sec
Training Progress:  58%|█████▊    | 117/200 [11:23<07:51,  5.68s/it]
[116/200][30/32] 	Loss_D: -0.0875 | Loss_G: -0.4717 | Iteration Time: 0.1465 sec
[117/200][0/32] 	Loss_D: -0.0640 | Loss_G: -0.4714 | Iteration Time: 0.1465 sec
[117/200][5/32] 	Loss_D: -0.0806 | Loss_G: -0.4986 | Iteration Time: 0.1410 sec
[117/200][10/32] 	Loss_D: -0.0727 | Loss_G: -0.4774 | Iteration Time: 0.1415 sec
[117/200][15/32] 	Loss_D: -0.0747 | Loss_G: -0.4840 | Iteration Time: 0.1420 sec
[117/200][20/32] 	Loss_D: -0.0909 | Loss_G: -0.4482 | Iteration Time: 0.1440 sec
[117/200][25/32] 	Loss_D: -0.0768 | Loss_G: -0.4244 | Iteration Time: 0.1430 sec
Training Progress:  59%|█████▉    | 118/200 [11:29<07:42,  5.64s/it]
[117/200][30/32] 	Loss_D: -0.0765 | Loss_G: -0.4860 | Iteration Time: 0.1445 sec
[118/200][0/32] 	Loss_D: -0.0837 | Loss_G: -0.4402 | Iteration Time: 0.1585 sec
[118/200][5/32] 	Loss_D: -0.0890 | Loss_G: -0.4654 | Iteration Time: 0.1440 sec
[118/200][10/32] 	Loss_D: -0.0620 | Loss_G: -0.4963 | Iteration Time: 0.1485 sec
[118/200][15/32] 	Loss_D: -0.0761 | Loss_G: -0.4916 | Iteration Time: 0.1455 sec
[118/200][20/32] 	Loss_D: -0.0874 | Loss_G: -0.4697 | Iteration Time: 0.1430 sec
[118/200][25/32] 	Loss_D: -0.0687 | Loss_G: -0.4773 | Iteration Time: 0.1560 sec
Training Progress:  60%|█████▉    | 119/200 [11:34<07:36,  5.63s/it]
[118/200][30/32] 	Loss_D: -0.0785 | Loss_G: -0.4264 | Iteration Time: 0.1445 sec
[119/200][0/32] 	Loss_D: -0.0764 | Loss_G: -0.4475 | Iteration Time: 0.1600 sec
[119/200][5/32] 	Loss_D: -0.0731 | Loss_G: -0.4354 | Iteration Time: 0.1500 sec
[119/200][10/32] 	Loss_D: -0.0765 | Loss_G: -0.4202 | Iteration Time: 0.1445 sec
[119/200][15/32] 	Loss_D: -0.0855 | Loss_G: -0.4744 | Iteration Time: 0.1441 sec
[119/200][20/32] 	Loss_D: -0.0850 | Loss_G: -0.4853 | Iteration Time: 0.1460 sec
[119/200][25/32] 	Loss_D: -0.0756 | Loss_G: -0.4744 | Iteration Time: 0.1445 sec
Training Progress:  60%|██████    | 120/200 [11:40<07:29,  5.62s/it]
[119/200][30/32] 	Loss_D: -0.0797 | Loss_G: -0.4280 | Iteration Time: 0.1445 sec
[120/200][0/32] 	Loss_D: -0.0795 | Loss_G: -0.4791 | Iteration Time: 0.1605 sec
[120/200][5/32] 	Loss_D: -0.0677 | Loss_G: -0.4289 | Iteration Time: 0.1400 sec
[120/200][10/32] 	Loss_D: -0.0858 | Loss_G: -0.4276 | Iteration Time: 0.1485 sec
[120/200][15/32] 	Loss_D: -0.0761 | Loss_G: -0.4315 | Iteration Time: 0.1440 sec
[120/200][20/32] 	Loss_D: -0.0825 | Loss_G: -0.4651 | Iteration Time: 0.1460 sec
[120/200][25/32] 	Loss_D: -0.0679 | Loss_G: -0.4251 | Iteration Time: 0.1435 sec
Training Progress:  60%|██████    | 121/200 [11:46<07:22,  5.61s/it]
[120/200][30/32] 	Loss_D: -0.0751 | Loss_G: -0.4415 | Iteration Time: 0.1450 sec
[121/200][0/32] 	Loss_D: -0.0740 | Loss_G: -0.4808 | Iteration Time: 0.1570 sec
[121/200][5/32] 	Loss_D: -0.0755 | Loss_G: -0.4183 | Iteration Time: 0.1460 sec
[121/200][10/32] 	Loss_D: -0.0765 | Loss_G: -0.4601 | Iteration Time: 0.1450 sec
[121/200][15/32] 	Loss_D: -0.0659 | Loss_G: -0.4867 | Iteration Time: 0.1450 sec
[121/200][20/32] 	Loss_D: -0.0588 | Loss_G: -0.5024 | Iteration Time: 0.1435 sec
[121/200][25/32] 	Loss_D: -0.0680 | Loss_G: -0.4283 | Iteration Time: 0.1490 sec
Training Progress:  61%|██████    | 122/200 [11:51<07:16,  5.59s/it]
[121/200][30/32] 	Loss_D: -0.0903 | Loss_G: -0.4285 | Iteration Time: 0.1445 sec
[122/200][0/32] 	Loss_D: -0.0742 | Loss_G: -0.4803 | Iteration Time: 0.1515 sec
[122/200][5/32] 	Loss_D: -0.0474 | Loss_G: -0.4849 | Iteration Time: 0.1400 sec
[122/200][10/32] 	Loss_D: -0.0881 | Loss_G: -0.4486 | Iteration Time: 0.1440 sec
[122/200][15/32] 	Loss_D: -0.0941 | Loss_G: -0.4868 | Iteration Time: 0.1490 sec
[122/200][20/32] 	Loss_D: -0.0736 | Loss_G: -0.4902 | Iteration Time: 0.1430 sec
[122/200][25/32] 	Loss_D: -0.0726 | Loss_G: -0.4248 | Iteration Time: 0.1470 sec
Training Progress:  62%|██████▏   | 123/200 [11:57<07:09,  5.58s/it]
[122/200][30/32] 	Loss_D: -0.0741 | Loss_G: -0.4276 | Iteration Time: 0.1410 sec
[123/200][0/32] 	Loss_D: -0.0768 | Loss_G: -0.4853 | Iteration Time: 0.1465 sec
[123/200][5/32] 	Loss_D: -0.0742 | Loss_G: -0.4963 | Iteration Time: 0.1485 sec
[123/200][10/32] 	Loss_D: -0.0795 | Loss_G: -0.4429 | Iteration Time: 0.1450 sec
[123/200][15/32] 	Loss_D: -0.0815 | Loss_G: -0.4639 | Iteration Time: 0.1440 sec
[123/200][20/32] 	Loss_D: -0.0794 | Loss_G: -0.4684 | Iteration Time: 0.1435 sec
[123/200][25/32] 	Loss_D: -0.0718 | Loss_G: -0.4814 | Iteration Time: 0.1435 sec
Training Progress:  62%|██████▏   | 124/200 [12:02<07:03,  5.58s/it]
[123/200][30/32] 	Loss_D: -0.0677 | Loss_G: -0.4433 | Iteration Time: 0.1495 sec
[124/200][0/32] 	Loss_D: -0.0844 | Loss_G: -0.4368 | Iteration Time: 0.1470 sec
[124/200][5/32] 	Loss_D: -0.0635 | Loss_G: -0.4732 | Iteration Time: 0.1435 sec
[124/200][10/32] 	Loss_D: -0.0717 | Loss_G: -0.4366 | Iteration Time: 0.1445 sec
[124/200][15/32] 	Loss_D: -0.0903 | Loss_G: -0.4325 | Iteration Time: 0.1454 sec
[124/200][20/32] 	Loss_D: -0.0713 | Loss_G: -0.4804 | Iteration Time: 0.1410 sec
[124/200][25/32] 	Loss_D: -0.0602 | Loss_G: -0.4285 | Iteration Time: 0.1445 sec
Training Progress:  62%|██████▎   | 125/200 [12:08<06:58,  5.58s/it]
[124/200][30/32] 	Loss_D: -0.0552 | Loss_G: -0.4727 | Iteration Time: 0.1470 sec
[125/200][0/32] 	Loss_D: -0.0918 | Loss_G: -0.4607 | Iteration Time: 0.1460 sec
Current scores at iteration 4000 | FID: 194.5837860107422 | IS: 2.361433506011963
No description has been provided for this image
[125/200][5/32] 	Loss_D: -0.0843 | Loss_G: -0.4592 | Iteration Time: 0.1395 sec
[125/200][10/32] 	Loss_D: -0.0914 | Loss_G: -0.4853 | Iteration Time: 0.1425 sec
[125/200][15/32] 	Loss_D: -0.0732 | Loss_G: -0.4778 | Iteration Time: 0.1425 sec
[125/200][20/32] 	Loss_D: -0.0720 | Loss_G: -0.4345 | Iteration Time: 0.1440 sec
[125/200][25/32] 	Loss_D: -0.0903 | Loss_G: -0.4229 | Iteration Time: 0.1445 sec
Training Progress:  63%|██████▎   | 126/200 [12:19<08:55,  7.24s/it]
[125/200][30/32] 	Loss_D: -0.0824 | Loss_G: -0.4703 | Iteration Time: 0.1440 sec
[126/200][0/32] 	Loss_D: -0.0732 | Loss_G: -0.4791 | Iteration Time: 0.1550 sec
[126/200][5/32] 	Loss_D: -0.0741 | Loss_G: -0.4876 | Iteration Time: 0.1455 sec
[126/200][10/32] 	Loss_D: -0.0741 | Loss_G: -0.4820 | Iteration Time: 0.1425 sec
[126/200][15/32] 	Loss_D: -0.0640 | Loss_G: -0.4284 | Iteration Time: 0.1430 sec
[126/200][20/32] 	Loss_D: -0.0699 | Loss_G: -0.4336 | Iteration Time: 0.1405 sec
[126/200][25/32] 	Loss_D: -0.0690 | Loss_G: -0.4766 | Iteration Time: 0.1465 sec
Training Progress:  64%|██████▎   | 127/200 [12:24<08:11,  6.73s/it]
[126/200][30/32] 	Loss_D: -0.0809 | Loss_G: -0.4711 | Iteration Time: 0.1456 sec
[127/200][0/32] 	Loss_D: -0.0856 | Loss_G: -0.4243 | Iteration Time: 0.1485 sec
[127/200][5/32] 	Loss_D: -0.0736 | Loss_G: -0.4523 | Iteration Time: 0.1415 sec
[127/200][10/32] 	Loss_D: -0.0701 | Loss_G: -0.4689 | Iteration Time: 0.1430 sec
[127/200][15/32] 	Loss_D: -0.0496 | Loss_G: -0.4818 | Iteration Time: 0.1490 sec
[127/200][20/32] 	Loss_D: -0.0673 | Loss_G: -0.4965 | Iteration Time: 0.1445 sec
[127/200][25/32] 	Loss_D: -0.0626 | Loss_G: -0.4865 | Iteration Time: 0.1455 sec
Training Progress:  64%|██████▍   | 128/200 [12:30<07:39,  6.38s/it]
[127/200][30/32] 	Loss_D: -0.0723 | Loss_G: -0.4795 | Iteration Time: 0.1450 sec
[128/200][0/32] 	Loss_D: -0.0693 | Loss_G: -0.4836 | Iteration Time: 0.1450 sec
[128/200][5/32] 	Loss_D: -0.0774 | Loss_G: -0.4268 | Iteration Time: 0.1465 sec
[128/200][10/32] 	Loss_D: -0.0675 | Loss_G: -0.4334 | Iteration Time: 0.1445 sec
[128/200][15/32] 	Loss_D: -0.0880 | Loss_G: -0.4272 | Iteration Time: 0.1451 sec
[128/200][20/32] 	Loss_D: -0.0825 | Loss_G: -0.4598 | Iteration Time: 0.1440 sec
[128/200][25/32] 	Loss_D: -0.0698 | Loss_G: -0.4913 | Iteration Time: 0.1435 sec
Training Progress:  64%|██████▍   | 129/200 [12:36<07:15,  6.14s/it]
[128/200][30/32] 	Loss_D: -0.0773 | Loss_G: -0.4763 | Iteration Time: 0.1475 sec
[129/200][0/32] 	Loss_D: -0.0720 | Loss_G: -0.4879 | Iteration Time: 0.1430 sec
[129/200][5/32] 	Loss_D: -0.0810 | Loss_G: -0.4519 | Iteration Time: 0.1450 sec
[129/200][10/32] 	Loss_D: -0.0756 | Loss_G: -0.4857 | Iteration Time: 0.1400 sec
[129/200][15/32] 	Loss_D: -0.0740 | Loss_G: -0.4279 | Iteration Time: 0.1435 sec
[129/200][20/32] 	Loss_D: -0.0657 | Loss_G: -0.4836 | Iteration Time: 0.1435 sec
[129/200][25/32] 	Loss_D: -0.0670 | Loss_G: -0.4903 | Iteration Time: 0.1410 sec
Training Progress:  65%|██████▌   | 130/200 [12:41<06:55,  5.94s/it]
[129/200][30/32] 	Loss_D: -0.0713 | Loss_G: -0.4851 | Iteration Time: 0.1400 sec
[130/200][0/32] 	Loss_D: -0.0691 | Loss_G: -0.4903 | Iteration Time: 0.1530 sec
[130/200][5/32] 	Loss_D: -0.0773 | Loss_G: -0.4310 | Iteration Time: 0.1430 sec
[130/200][10/32] 	Loss_D: -0.0726 | Loss_G: -0.4993 | Iteration Time: 0.1425 sec
[130/200][15/32] 	Loss_D: -0.0754 | Loss_G: -0.4779 | Iteration Time: 0.1465 sec
[130/200][20/32] 	Loss_D: -0.0808 | Loss_G: -0.4793 | Iteration Time: 0.1455 sec
[130/200][25/32] 	Loss_D: -0.0907 | Loss_G: -0.4509 | Iteration Time: 0.1455 sec
Training Progress:  66%|██████▌   | 131/200 [12:47<06:41,  5.82s/it]
[130/200][30/32] 	Loss_D: -0.0778 | Loss_G: -0.4349 | Iteration Time: 0.1415 sec
[131/200][0/32] 	Loss_D: -0.0872 | Loss_G: -0.4392 | Iteration Time: 0.1505 sec
[131/200][5/32] 	Loss_D: -0.0621 | Loss_G: -0.4844 | Iteration Time: 0.1455 sec
[131/200][10/32] 	Loss_D: -0.0642 | Loss_G: -0.4257 | Iteration Time: 0.1395 sec
[131/200][15/32] 	Loss_D: -0.0832 | Loss_G: -0.4753 | Iteration Time: 0.1435 sec
[131/200][20/32] 	Loss_D: -0.0752 | Loss_G: -0.4283 | Iteration Time: 0.1445 sec
[131/200][25/32] 	Loss_D: -0.0701 | Loss_G: -0.4332 | Iteration Time: 0.1410 sec
Training Progress:  66%|██████▌   | 132/200 [12:52<06:30,  5.74s/it]
[131/200][30/32] 	Loss_D: -0.0853 | Loss_G: -0.4518 | Iteration Time: 0.1455 sec
[132/200][0/32] 	Loss_D: -0.0903 | Loss_G: -0.4404 | Iteration Time: 0.1485 sec
[132/200][5/32] 	Loss_D: -0.0811 | Loss_G: -0.4714 | Iteration Time: 0.1410 sec
[132/200][10/32] 	Loss_D: -0.0757 | Loss_G: -0.4706 | Iteration Time: 0.1485 sec
[132/200][15/32] 	Loss_D: -0.0773 | Loss_G: -0.4621 | Iteration Time: 0.1401 sec
[132/200][20/32] 	Loss_D: -0.0783 | Loss_G: -0.4814 | Iteration Time: 0.1403 sec
[132/200][25/32] 	Loss_D: -0.0818 | Loss_G: -0.4302 | Iteration Time: 0.1390 sec
Training Progress:  66%|██████▋   | 133/200 [12:58<06:19,  5.67s/it]
[132/200][30/32] 	Loss_D: -0.0742 | Loss_G: -0.4424 | Iteration Time: 0.1400 sec
[133/200][0/32] 	Loss_D: -0.0934 | Loss_G: -0.4351 | Iteration Time: 0.1495 sec
[133/200][5/32] 	Loss_D: -0.0843 | Loss_G: -0.4807 | Iteration Time: 0.1415 sec
[133/200][10/32] 	Loss_D: -0.0816 | Loss_G: -0.4329 | Iteration Time: 0.1420 sec
[133/200][15/32] 	Loss_D: -0.0960 | Loss_G: -0.4358 | Iteration Time: 0.1470 sec
[133/200][20/32] 	Loss_D: -0.0845 | Loss_G: -0.4295 | Iteration Time: 0.1405 sec
[133/200][25/32] 	Loss_D: -0.0681 | Loss_G: -0.4805 | Iteration Time: 0.1460 sec
Training Progress:  67%|██████▋   | 134/200 [13:03<06:11,  5.63s/it]
[133/200][30/32] 	Loss_D: -0.0709 | Loss_G: -0.4707 | Iteration Time: 0.1418 sec
[134/200][0/32] 	Loss_D: -0.0646 | Loss_G: -0.4855 | Iteration Time: 0.1490 sec
[134/200][5/32] 	Loss_D: -0.0733 | Loss_G: -0.4259 | Iteration Time: 0.1460 sec
[134/200][10/32] 	Loss_D: -0.0815 | Loss_G: -0.4902 | Iteration Time: 0.1450 sec
[134/200][15/32] 	Loss_D: -0.0892 | Loss_G: -0.4347 | Iteration Time: 0.1435 sec
[134/200][20/32] 	Loss_D: -0.0955 | Loss_G: -0.4656 | Iteration Time: 0.1440 sec
[134/200][25/32] 	Loss_D: -0.0862 | Loss_G: -0.4800 | Iteration Time: 0.1440 sec
Training Progress:  68%|██████▊   | 135/200 [13:09<06:04,  5.61s/it]
[134/200][30/32] 	Loss_D: -0.0681 | Loss_G: -0.4847 | Iteration Time: 0.1455 sec
[135/200][0/32] 	Loss_D: -0.0725 | Loss_G: -0.4879 | Iteration Time: 0.1450 sec
[135/200][5/32] 	Loss_D: -0.0688 | Loss_G: -0.5047 | Iteration Time: 0.1415 sec
[135/200][10/32] 	Loss_D: -0.0387 | Loss_G: -0.4675 | Iteration Time: 0.1425 sec
[135/200][15/32] 	Loss_D: -0.0725 | Loss_G: -0.4208 | Iteration Time: 0.1455 sec
[135/200][20/32] 	Loss_D: -0.0665 | Loss_G: -0.4843 | Iteration Time: 0.1475 sec
[135/200][25/32] 	Loss_D: -0.0802 | Loss_G: -0.4296 | Iteration Time: 0.1445 sec
Training Progress:  68%|██████▊   | 136/200 [13:14<05:58,  5.60s/it]
[135/200][30/32] 	Loss_D: -0.0658 | Loss_G: -0.5026 | Iteration Time: 0.1460 sec
[136/200][0/32] 	Loss_D: -0.0739 | Loss_G: -0.4435 | Iteration Time: 0.1605 sec
[136/200][5/32] 	Loss_D: -0.0690 | Loss_G: -0.4748 | Iteration Time: 0.1450 sec
[136/200][10/32] 	Loss_D: -0.0622 | Loss_G: -0.4880 | Iteration Time: 0.1480 sec
[136/200][15/32] 	Loss_D: -0.0810 | Loss_G: -0.4785 | Iteration Time: 0.1440 sec
[136/200][20/32] 	Loss_D: -0.0687 | Loss_G: -0.4871 | Iteration Time: 0.1425 sec
[136/200][25/32] 	Loss_D: -0.0622 | Loss_G: -0.4775 | Iteration Time: 0.1435 sec
Training Progress:  68%|██████▊   | 137/200 [13:20<05:51,  5.58s/it]
[136/200][30/32] 	Loss_D: -0.0637 | Loss_G: -0.4255 | Iteration Time: 0.1405 sec
[137/200][0/32] 	Loss_D: -0.0725 | Loss_G: -0.4815 | Iteration Time: 0.1560 sec
[137/200][5/32] 	Loss_D: -0.0639 | Loss_G: -0.4321 | Iteration Time: 0.1400 sec
[137/200][10/32] 	Loss_D: -0.0638 | Loss_G: -0.4891 | Iteration Time: 0.1437 sec
[137/200][15/32] 	Loss_D: -0.0630 | Loss_G: -0.4927 | Iteration Time: 0.1488 sec
[137/200][20/32] 	Loss_D: -0.0684 | Loss_G: -0.4198 | Iteration Time: 0.1420 sec
[137/200][25/32] 	Loss_D: -0.0680 | Loss_G: -0.4263 | Iteration Time: 0.1445 sec
Training Progress:  69%|██████▉   | 138/200 [13:25<05:45,  5.57s/it]
[137/200][30/32] 	Loss_D: -0.0702 | Loss_G: -0.4876 | Iteration Time: 0.1450 sec
[138/200][0/32] 	Loss_D: -0.0748 | Loss_G: -0.4713 | Iteration Time: 0.1485 sec
[138/200][5/32] 	Loss_D: -0.0691 | Loss_G: -0.4937 | Iteration Time: 0.1480 sec
[138/200][10/32] 	Loss_D: -0.0797 | Loss_G: -0.4262 | Iteration Time: 0.1415 sec
[138/200][15/32] 	Loss_D: -0.0758 | Loss_G: -0.4802 | Iteration Time: 0.1440 sec
[138/200][20/32] 	Loss_D: -0.0845 | Loss_G: -0.4577 | Iteration Time: 0.1460 sec
[138/200][25/32] 	Loss_D: -0.0748 | Loss_G: -0.4819 | Iteration Time: 0.1409 sec
Training Progress:  70%|██████▉   | 139/200 [13:31<05:38,  5.55s/it]
[138/200][30/32] 	Loss_D: -0.0781 | Loss_G: -0.4741 | Iteration Time: 0.1455 sec
[139/200][0/32] 	Loss_D: -0.0968 | Loss_G: -0.4384 | Iteration Time: 0.1570 sec
[139/200][5/32] 	Loss_D: -0.0635 | Loss_G: -0.4840 | Iteration Time: 0.1435 sec
[139/200][10/32] 	Loss_D: -0.0800 | Loss_G: -0.4758 | Iteration Time: 0.1445 sec
[139/200][15/32] 	Loss_D: -0.0755 | Loss_G: -0.4809 | Iteration Time: 0.1430 sec
[139/200][20/32] 	Loss_D: -0.0843 | Loss_G: -0.4325 | Iteration Time: 0.1440 sec
[139/200][25/32] 	Loss_D: -0.0619 | Loss_G: -0.4899 | Iteration Time: 0.1440 sec
Training Progress:  70%|███████   | 140/200 [13:36<05:32,  5.55s/it]
[139/200][30/32] 	Loss_D: -0.0771 | Loss_G: -0.4908 | Iteration Time: 0.1440 sec
[140/200][0/32] 	Loss_D: -0.0836 | Loss_G: -0.4344 | Iteration Time: 0.1555 sec
[140/200][5/32] 	Loss_D: -0.0894 | Loss_G: -0.4430 | Iteration Time: 0.1530 sec
[140/200][10/32] 	Loss_D: -0.0602 | Loss_G: -0.4470 | Iteration Time: 0.1560 sec
[140/200][15/32] 	Loss_D: -0.0730 | Loss_G: -0.4648 | Iteration Time: 0.1472 sec
[140/200][20/32] 	Loss_D: -0.0803 | Loss_G: -0.4763 | Iteration Time: 0.1575 sec
Current scores at iteration 4500 | FID: 171.5572509765625 | IS: 2.464463710784912
No description has been provided for this image
[140/200][25/32] 	Loss_D: -0.0624 | Loss_G: -0.4812 | Iteration Time: 0.1430 sec
Training Progress:  70%|███████   | 141/200 [13:48<07:07,  7.25s/it]
[140/200][30/32] 	Loss_D: -0.0964 | Loss_G: -0.4857 | Iteration Time: 0.1455 sec
[141/200][0/32] 	Loss_D: -0.0847 | Loss_G: -0.4347 | Iteration Time: 0.1598 sec
[141/200][5/32] 	Loss_D: -0.0721 | Loss_G: -0.4935 | Iteration Time: 0.1440 sec
[141/200][10/32] 	Loss_D: -0.0605 | Loss_G: -0.4264 | Iteration Time: 0.1500 sec
[141/200][15/32] 	Loss_D: -0.0721 | Loss_G: -0.4262 | Iteration Time: 0.1485 sec
[141/200][20/32] 	Loss_D: -0.0561 | Loss_G: -0.4318 | Iteration Time: 0.1475 sec
[141/200][25/32] 	Loss_D: -0.0668 | Loss_G: -0.4281 | Iteration Time: 0.1505 sec
Training Progress:  71%|███████   | 142/200 [13:53<06:33,  6.79s/it]
[141/200][30/32] 	Loss_D: -0.0569 | Loss_G: -0.4281 | Iteration Time: 0.1445 sec
[142/200][0/32] 	Loss_D: -0.0675 | Loss_G: -0.4810 | Iteration Time: 0.1470 sec
[142/200][5/32] 	Loss_D: -0.0671 | Loss_G: -0.4786 | Iteration Time: 0.1465 sec
[142/200][10/32] 	Loss_D: -0.0734 | Loss_G: -0.4977 | Iteration Time: 0.1400 sec
[142/200][15/32] 	Loss_D: -0.0862 | Loss_G: -0.4295 | Iteration Time: 0.1425 sec
[142/200][20/32] 	Loss_D: -0.0731 | Loss_G: -0.4878 | Iteration Time: 0.1475 sec
[142/200][25/32] 	Loss_D: -0.0766 | Loss_G: -0.4813 | Iteration Time: 0.1425 sec
Training Progress:  72%|███████▏  | 143/200 [13:59<06:05,  6.41s/it]
[142/200][30/32] 	Loss_D: -0.0708 | Loss_G: -0.4706 | Iteration Time: 0.1445 sec
[143/200][0/32] 	Loss_D: -0.0919 | Loss_G: -0.4449 | Iteration Time: 0.1540 sec
[143/200][5/32] 	Loss_D: -0.0681 | Loss_G: -0.4932 | Iteration Time: 0.1420 sec
[143/200][10/32] 	Loss_D: -0.0688 | Loss_G: -0.4813 | Iteration Time: 0.1475 sec
[143/200][15/32] 	Loss_D: -0.0834 | Loss_G: -0.4739 | Iteration Time: 0.1535 sec
[143/200][20/32] 	Loss_D: -0.0866 | Loss_G: -0.4530 | Iteration Time: 0.1485 sec
[143/200][25/32] 	Loss_D: -0.0710 | Loss_G: -0.4267 | Iteration Time: 0.1480 sec
Training Progress:  72%|███████▏  | 144/200 [14:05<05:47,  6.20s/it]
[143/200][30/32] 	Loss_D: -0.0721 | Loss_G: -0.4882 | Iteration Time: 0.1480 sec
[144/200][0/32] 	Loss_D: -0.0821 | Loss_G: -0.4876 | Iteration Time: 0.1535 sec
[144/200][5/32] 	Loss_D: -0.0646 | Loss_G: -0.4920 | Iteration Time: 0.1395 sec
[144/200][10/32] 	Loss_D: -0.0735 | Loss_G: -0.4711 | Iteration Time: 0.1395 sec
[144/200][15/32] 	Loss_D: -0.0604 | Loss_G: -0.4777 | Iteration Time: 0.1445 sec
[144/200][20/32] 	Loss_D: -0.0914 | Loss_G: -0.4444 | Iteration Time: 0.1430 sec
[144/200][25/32] 	Loss_D: -0.0750 | Loss_G: -0.4796 | Iteration Time: 0.1440 sec
Training Progress:  72%|███████▎  | 145/200 [14:10<05:29,  6.00s/it]
[144/200][30/32] 	Loss_D: -0.0671 | Loss_G: -0.4743 | Iteration Time: 0.1475 sec
[145/200][0/32] 	Loss_D: -0.0811 | Loss_G: -0.4356 | Iteration Time: 0.1470 sec
[145/200][5/32] 	Loss_D: -0.0766 | Loss_G: -0.4872 | Iteration Time: 0.1525 sec
[145/200][10/32] 	Loss_D: -0.0856 | Loss_G: -0.4478 | Iteration Time: 0.1430 sec
[145/200][15/32] 	Loss_D: -0.0735 | Loss_G: -0.4826 | Iteration Time: 0.1425 sec
[145/200][20/32] 	Loss_D: -0.0744 | Loss_G: -0.4909 | Iteration Time: 0.1440 sec
[145/200][25/32] 	Loss_D: -0.0820 | Loss_G: -0.4735 | Iteration Time: 0.1420 sec
Training Progress:  73%|███████▎  | 146/200 [14:16<05:17,  5.88s/it]
[145/200][30/32] 	Loss_D: -0.0738 | Loss_G: -0.4871 | Iteration Time: 0.1470 sec
[146/200][0/32] 	Loss_D: -0.0797 | Loss_G: -0.4247 | Iteration Time: 0.1445 sec
[146/200][5/32] 	Loss_D: -0.0737 | Loss_G: -0.4866 | Iteration Time: 0.1425 sec
[146/200][10/32] 	Loss_D: -0.0715 | Loss_G: -0.4686 | Iteration Time: 0.1425 sec
[146/200][15/32] 	Loss_D: -0.0826 | Loss_G: -0.4502 | Iteration Time: 0.1435 sec
[146/200][20/32] 	Loss_D: -0.0766 | Loss_G: -0.4573 | Iteration Time: 0.1460 sec
[146/200][25/32] 	Loss_D: -0.0616 | Loss_G: -0.4264 | Iteration Time: 0.1445 sec
Training Progress:  74%|███████▎  | 147/200 [14:21<05:06,  5.78s/it]
[146/200][30/32] 	Loss_D: -0.0728 | Loss_G: -0.4485 | Iteration Time: 0.1470 sec
[147/200][0/32] 	Loss_D: -0.0739 | Loss_G: -0.4896 | Iteration Time: 0.1545 sec
[147/200][5/32] 	Loss_D: -0.0601 | Loss_G: -0.4883 | Iteration Time: 0.1425 sec
[147/200][10/32] 	Loss_D: -0.0662 | Loss_G: -0.4692 | Iteration Time: 0.1465 sec
[147/200][15/32] 	Loss_D: -0.0743 | Loss_G: -0.4692 | Iteration Time: 0.1420 sec
[147/200][20/32] 	Loss_D: -0.0744 | Loss_G: -0.4880 | Iteration Time: 0.1445 sec
[147/200][25/32] 	Loss_D: -0.0857 | Loss_G: -0.4747 | Iteration Time: 0.1410 sec
Training Progress:  74%|███████▍  | 148/200 [14:27<04:56,  5.71s/it]
[147/200][30/32] 	Loss_D: -0.0742 | Loss_G: -0.4590 | Iteration Time: 0.1485 sec
[148/200][0/32] 	Loss_D: -0.0663 | Loss_G: -0.4775 | Iteration Time: 0.1565 sec
[148/200][5/32] 	Loss_D: -0.0832 | Loss_G: -0.4668 | Iteration Time: 0.1425 sec
[148/200][10/32] 	Loss_D: -0.0844 | Loss_G: -0.4539 | Iteration Time: 0.1425 sec
[148/200][15/32] 	Loss_D: -0.0665 | Loss_G: -0.4953 | Iteration Time: 0.1450 sec
[148/200][20/32] 	Loss_D: -0.0554 | Loss_G: -0.4929 | Iteration Time: 0.1415 sec
[148/200][25/32] 	Loss_D: -0.0697 | Loss_G: -0.4836 | Iteration Time: 0.1470 sec
Training Progress:  74%|███████▍  | 149/200 [14:32<04:48,  5.66s/it]
[148/200][30/32] 	Loss_D: -0.0694 | Loss_G: -0.4935 | Iteration Time: 0.1400 sec
[149/200][0/32] 	Loss_D: -0.0842 | Loss_G: -0.4389 | Iteration Time: 0.1480 sec
[149/200][5/32] 	Loss_D: -0.0703 | Loss_G: -0.4279 | Iteration Time: 0.1465 sec
[149/200][10/32] 	Loss_D: -0.0875 | Loss_G: -0.4348 | Iteration Time: 0.1415 sec
[149/200][15/32] 	Loss_D: -0.0781 | Loss_G: -0.4718 | Iteration Time: 0.1440 sec
[149/200][20/32] 	Loss_D: -0.0864 | Loss_G: -0.4621 | Iteration Time: 0.1435 sec
[149/200][25/32] 	Loss_D: -0.0666 | Loss_G: -0.4292 | Iteration Time: 0.1415 sec
Training Progress:  75%|███████▌  | 150/200 [14:38<04:41,  5.62s/it]
[149/200][30/32] 	Loss_D: -0.0562 | Loss_G: -0.4669 | Iteration Time: 0.1470 sec
[150/200][0/32] 	Loss_D: -0.0848 | Loss_G: -0.4625 | Iteration Time: 0.1465 sec
[150/200][5/32] 	Loss_D: -0.0734 | Loss_G: -0.4791 | Iteration Time: 0.1425 sec
[150/200][10/32] 	Loss_D: -0.0713 | Loss_G: -0.4668 | Iteration Time: 0.1440 sec
[150/200][15/32] 	Loss_D: -0.0684 | Loss_G: -0.4805 | Iteration Time: 0.1455 sec
[150/200][20/32] 	Loss_D: -0.0795 | Loss_G: -0.4243 | Iteration Time: 0.1467 sec
[150/200][25/32] 	Loss_D: -0.0777 | Loss_G: -0.4790 | Iteration Time: 0.1420 sec
Training Progress:  76%|███████▌  | 151/200 [14:43<04:34,  5.60s/it]
[150/200][30/32] 	Loss_D: -0.0694 | Loss_G: -0.4817 | Iteration Time: 0.1435 sec
[151/200][0/32] 	Loss_D: -0.0790 | Loss_G: -0.4504 | Iteration Time: 0.1610 sec
[151/200][5/32] 	Loss_D: -0.0693 | Loss_G: -0.4303 | Iteration Time: 0.1395 sec
[151/200][10/32] 	Loss_D: -0.0886 | Loss_G: -0.4769 | Iteration Time: 0.1446 sec
[151/200][15/32] 	Loss_D: -0.0817 | Loss_G: -0.4733 | Iteration Time: 0.1430 sec
[151/200][20/32] 	Loss_D: -0.0722 | Loss_G: -0.4321 | Iteration Time: 0.1460 sec
[151/200][25/32] 	Loss_D: -0.0784 | Loss_G: -0.4516 | Iteration Time: 0.1465 sec
Training Progress:  76%|███████▌  | 152/200 [14:49<04:27,  5.57s/it]
[151/200][30/32] 	Loss_D: -0.0694 | Loss_G: -0.4851 | Iteration Time: 0.1425 sec
[152/200][0/32] 	Loss_D: -0.0615 | Loss_G: -0.4832 | Iteration Time: 0.1480 sec
[152/200][5/32] 	Loss_D: -0.0617 | Loss_G: -0.4303 | Iteration Time: 0.1420 sec
[152/200][10/32] 	Loss_D: -0.0853 | Loss_G: -0.4945 | Iteration Time: 0.1420 sec
[152/200][15/32] 	Loss_D: -0.0609 | Loss_G: -0.4345 | Iteration Time: 0.1460 sec
[152/200][20/32] 	Loss_D: -0.0669 | Loss_G: -0.4423 | Iteration Time: 0.1430 sec
[152/200][25/32] 	Loss_D: -0.0778 | Loss_G: -0.4919 | Iteration Time: 0.1400 sec
Training Progress:  76%|███████▋  | 153/200 [14:55<04:21,  5.56s/it]
[152/200][30/32] 	Loss_D: -0.0767 | Loss_G: -0.4822 | Iteration Time: 0.1500 sec
[153/200][0/32] 	Loss_D: -0.0846 | Loss_G: -0.4478 | Iteration Time: 0.1445 sec
[153/200][5/32] 	Loss_D: -0.0806 | Loss_G: -0.4342 | Iteration Time: 0.1446 sec
[153/200][10/32] 	Loss_D: -0.0783 | Loss_G: -0.4779 | Iteration Time: 0.1435 sec
[153/200][15/32] 	Loss_D: -0.0766 | Loss_G: -0.4587 | Iteration Time: 0.1455 sec
[153/200][20/32] 	Loss_D: -0.0788 | Loss_G: -0.4815 | Iteration Time: 0.1450 sec
[153/200][25/32] 	Loss_D: -0.0722 | Loss_G: -0.4822 | Iteration Time: 0.1405 sec
Training Progress:  77%|███████▋  | 154/200 [15:00<04:16,  5.57s/it]
[153/200][30/32] 	Loss_D: -0.0647 | Loss_G: -0.4266 | Iteration Time: 0.1520 sec
[154/200][0/32] 	Loss_D: -0.0632 | Loss_G: -0.4880 | Iteration Time: 0.1500 sec
[154/200][5/32] 	Loss_D: -0.0669 | Loss_G: -0.4842 | Iteration Time: 0.1415 sec
[154/200][10/32] 	Loss_D: -0.0850 | Loss_G: -0.4458 | Iteration Time: 0.1357 sec
[154/200][15/32] 	Loss_D: -0.0830 | Loss_G: -0.4770 | Iteration Time: 0.1295 sec
[154/200][20/32] 	Loss_D: -0.0771 | Loss_G: -0.4728 | Iteration Time: 0.1520 sec
[154/200][25/32] 	Loss_D: -0.0810 | Loss_G: -0.4266 | Iteration Time: 0.1380 sec
Training Progress:  78%|███████▊  | 155/200 [15:06<04:09,  5.54s/it]
[154/200][30/32] 	Loss_D: -0.0748 | Loss_G: -0.4762 | Iteration Time: 0.1450 sec
[155/200][0/32] 	Loss_D: -0.0792 | Loss_G: -0.4748 | Iteration Time: 0.1520 sec
[155/200][5/32] 	Loss_D: -0.0772 | Loss_G: -0.4261 | Iteration Time: 0.1450 sec
[155/200][10/32] 	Loss_D: -0.0673 | Loss_G: -0.4899 | Iteration Time: 0.1450 sec
[155/200][15/32] 	Loss_D: -0.0670 | Loss_G: -0.4904 | Iteration Time: 0.1465 sec
[155/200][20/32] 	Loss_D: -0.0676 | Loss_G: -0.4950 | Iteration Time: 0.1495 sec
[155/200][25/32] 	Loss_D: -0.0522 | Loss_G: -0.4758 | Iteration Time: 0.1655 sec
Training Progress:  78%|███████▊  | 156/200 [15:11<04:05,  5.57s/it]
[155/200][30/32] 	Loss_D: -0.0815 | Loss_G: -0.4500 | Iteration Time: 0.1010 sec
[156/200][0/32] 	Loss_D: -0.0805 | Loss_G: -0.4813 | Iteration Time: 0.1030 sec
[156/200][5/32] 	Loss_D: -0.0713 | Loss_G: -0.4272 | Iteration Time: 0.0900 sec
Current scores at iteration 5000 | FID: 172.36561584472656 | IS: 2.257277011871338
No description has been provided for this image
[156/200][10/32] 	Loss_D: -0.0711 | Loss_G: -0.4901 | Iteration Time: 0.0990 sec
[156/200][15/32] 	Loss_D: -0.0634 | Loss_G: -0.4960 | Iteration Time: 0.0915 sec
[156/200][20/32] 	Loss_D: -0.0674 | Loss_G: -0.4299 | Iteration Time: 0.0875 sec
[156/200][25/32] 	Loss_D: -0.0700 | Loss_G: -0.4936 | Iteration Time: 0.0915 sec
Training Progress:  78%|███████▊  | 157/200 [15:20<04:43,  6.58s/it]
[156/200][30/32] 	Loss_D: -0.0842 | Loss_G: -0.4332 | Iteration Time: 0.0880 sec
[157/200][0/32] 	Loss_D: -0.0758 | Loss_G: -0.4726 | Iteration Time: 0.0910 sec
[157/200][5/32] 	Loss_D: -0.0666 | Loss_G: -0.4902 | Iteration Time: 0.0879 sec
[157/200][10/32] 	Loss_D: -0.0689 | Loss_G: -0.4314 | Iteration Time: 0.0900 sec
[157/200][15/32] 	Loss_D: -0.0714 | Loss_G: -0.4341 | Iteration Time: 0.0895 sec
[157/200][20/32] 	Loss_D: -0.0824 | Loss_G: -0.4586 | Iteration Time: 0.0900 sec
[157/200][25/32] 	Loss_D: -0.0714 | Loss_G: -0.4761 | Iteration Time: 0.0890 sec
Training Progress:  79%|███████▉  | 158/200 [15:24<04:00,  5.72s/it]
[157/200][30/32] 	Loss_D: -0.0752 | Loss_G: -0.4817 | Iteration Time: 0.0915 sec
[158/200][0/32] 	Loss_D: -0.0756 | Loss_G: -0.4749 | Iteration Time: 0.0880 sec
[158/200][5/32] 	Loss_D: -0.0826 | Loss_G: -0.4798 | Iteration Time: 0.0875 sec
[158/200][10/32] 	Loss_D: -0.0548 | Loss_G: -0.4871 | Iteration Time: 0.0895 sec
[158/200][15/32] 	Loss_D: -0.0599 | Loss_G: -0.4226 | Iteration Time: 0.0915 sec
[158/200][20/32] 	Loss_D: -0.0759 | Loss_G: -0.4773 | Iteration Time: 0.0915 sec
[158/200][25/32] 	Loss_D: -0.0818 | Loss_G: -0.4937 | Iteration Time: 0.0895 sec
Training Progress:  80%|███████▉  | 159/200 [15:28<03:30,  5.13s/it]
[158/200][30/32] 	Loss_D: -0.0854 | Loss_G: -0.4349 | Iteration Time: 0.0885 sec
[159/200][0/32] 	Loss_D: -0.0692 | Loss_G: -0.4422 | Iteration Time: 0.0920 sec
[159/200][5/32] 	Loss_D: -0.0872 | Loss_G: -0.4783 | Iteration Time: 0.0885 sec
[159/200][10/32] 	Loss_D: -0.0798 | Loss_G: -0.4636 | Iteration Time: 0.0895 sec
[159/200][15/32] 	Loss_D: -0.0743 | Loss_G: -0.4739 | Iteration Time: 0.0890 sec
[159/200][20/32] 	Loss_D: -0.0737 | Loss_G: -0.4835 | Iteration Time: 0.0890 sec
[159/200][25/32] 	Loss_D: -0.0710 | Loss_G: -0.4787 | Iteration Time: 0.0910 sec
Training Progress:  80%|████████  | 160/200 [15:31<03:08,  4.71s/it]
[159/200][30/32] 	Loss_D: -0.0718 | Loss_G: -0.4720 | Iteration Time: 0.0895 sec
[160/200][0/32] 	Loss_D: -0.0725 | Loss_G: -0.4922 | Iteration Time: 0.0890 sec
[160/200][5/32] 	Loss_D: -0.0898 | Loss_G: -0.4621 | Iteration Time: 0.0890 sec
[160/200][10/32] 	Loss_D: -0.0756 | Loss_G: -0.4686 | Iteration Time: 0.0915 sec
[160/200][15/32] 	Loss_D: -0.0734 | Loss_G: -0.4782 | Iteration Time: 0.0905 sec
[160/200][20/32] 	Loss_D: -0.0863 | Loss_G: -0.4467 | Iteration Time: 0.0915 sec
[160/200][25/32] 	Loss_D: -0.0738 | Loss_G: -0.4213 | Iteration Time: 0.0895 sec
Training Progress:  80%|████████  | 161/200 [15:35<02:52,  4.42s/it]
[160/200][30/32] 	Loss_D: -0.0612 | Loss_G: -0.4309 | Iteration Time: 0.0890 sec
[161/200][0/32] 	Loss_D: -0.0820 | Loss_G: -0.4420 | Iteration Time: 0.0895 sec
[161/200][5/32] 	Loss_D: -0.0498 | Loss_G: -0.4924 | Iteration Time: 0.0890 sec
[161/200][10/32] 	Loss_D: -0.0474 | Loss_G: -0.4832 | Iteration Time: 0.0900 sec
[161/200][15/32] 	Loss_D: -0.0681 | Loss_G: -0.4967 | Iteration Time: 0.0900 sec
[161/200][20/32] 	Loss_D: -0.0782 | Loss_G: -0.4896 | Iteration Time: 0.0890 sec
[161/200][25/32] 	Loss_D: -0.0668 | Loss_G: -0.4691 | Iteration Time: 0.0895 sec
Training Progress:  81%|████████  | 162/200 [15:39<02:40,  4.22s/it]
[161/200][30/32] 	Loss_D: -0.0672 | Loss_G: -0.4423 | Iteration Time: 0.0945 sec
[162/200][0/32] 	Loss_D: -0.0643 | Loss_G: -0.4865 | Iteration Time: 0.0920 sec
[162/200][5/32] 	Loss_D: -0.0686 | Loss_G: -0.4287 | Iteration Time: 0.0925 sec
[162/200][10/32] 	Loss_D: -0.0873 | Loss_G: -0.4567 | Iteration Time: 0.0940 sec
[162/200][15/32] 	Loss_D: -0.0629 | Loss_G: -0.4904 | Iteration Time: 0.1010 sec
[162/200][20/32] 	Loss_D: -0.0768 | Loss_G: -0.4770 | Iteration Time: 0.1220 sec
[162/200][25/32] 	Loss_D: -0.0632 | Loss_G: -0.4905 | Iteration Time: 0.1375 sec
Training Progress:  82%|████████▏ | 163/200 [15:43<02:38,  4.29s/it]
[162/200][30/32] 	Loss_D: -0.0597 | Loss_G: -0.4222 | Iteration Time: 0.1460 sec
[163/200][0/32] 	Loss_D: -0.0568 | Loss_G: -0.4922 | Iteration Time: 0.1445 sec
[163/200][5/32] 	Loss_D: -0.0850 | Loss_G: -0.4233 | Iteration Time: 0.1470 sec
[163/200][10/32] 	Loss_D: -0.0783 | Loss_G: -0.4894 | Iteration Time: 0.1405 sec
[163/200][15/32] 	Loss_D: -0.0769 | Loss_G: -0.4358 | Iteration Time: 0.1505 sec
[163/200][20/32] 	Loss_D: -0.0691 | Loss_G: -0.4373 | Iteration Time: 0.1525 sec
[163/200][25/32] 	Loss_D: -0.0837 | Loss_G: -0.4752 | Iteration Time: 0.1495 sec
Training Progress:  82%|████████▏ | 164/200 [15:49<02:49,  4.71s/it]
[163/200][30/32] 	Loss_D: -0.0535 | Loss_G: -0.4895 | Iteration Time: 0.1460 sec
[164/200][0/32] 	Loss_D: -0.0796 | Loss_G: -0.4699 | Iteration Time: 0.1570 sec
[164/200][5/32] 	Loss_D: -0.0793 | Loss_G: -0.4345 | Iteration Time: 0.1430 sec
[164/200][10/32] 	Loss_D: -0.0718 | Loss_G: -0.5009 | Iteration Time: 0.1475 sec
[164/200][15/32] 	Loss_D: -0.0740 | Loss_G: -0.4766 | Iteration Time: 0.1435 sec
[164/200][20/32] 	Loss_D: -0.0689 | Loss_G: -0.4794 | Iteration Time: 0.1425 sec
[164/200][25/32] 	Loss_D: -0.0731 | Loss_G: -0.4870 | Iteration Time: 0.1450 sec
Training Progress:  82%|████████▎ | 165/200 [15:55<02:54,  4.97s/it]
[164/200][30/32] 	Loss_D: -0.0794 | Loss_G: -0.4328 | Iteration Time: 0.1400 sec
[165/200][0/32] 	Loss_D: -0.0849 | Loss_G: -0.4627 | Iteration Time: 0.1460 sec
[165/200][5/32] 	Loss_D: -0.0900 | Loss_G: -0.4706 | Iteration Time: 0.1450 sec
[165/200][10/32] 	Loss_D: -0.0798 | Loss_G: -0.4726 | Iteration Time: 0.1435 sec
[165/200][15/32] 	Loss_D: -0.0782 | Loss_G: -0.4376 | Iteration Time: 0.1475 sec
[165/200][20/32] 	Loss_D: -0.0850 | Loss_G: -0.4783 | Iteration Time: 0.1455 sec
[165/200][25/32] 	Loss_D: -0.0677 | Loss_G: -0.4865 | Iteration Time: 0.1450 sec
Training Progress:  83%|████████▎ | 166/200 [16:00<02:55,  5.15s/it]
[165/200][30/32] 	Loss_D: -0.0843 | Loss_G: -0.4468 | Iteration Time: 0.1425 sec
[166/200][0/32] 	Loss_D: -0.0598 | Loss_G: -0.4793 | Iteration Time: 0.1395 sec
[166/200][5/32] 	Loss_D: -0.0777 | Loss_G: -0.4707 | Iteration Time: 0.1380 sec
[166/200][10/32] 	Loss_D: -0.0709 | Loss_G: -0.4309 | Iteration Time: 0.1375 sec
[166/200][15/32] 	Loss_D: -0.0665 | Loss_G: -0.4401 | Iteration Time: 0.1400 sec
[166/200][20/32] 	Loss_D: -0.0852 | Loss_G: -0.4510 | Iteration Time: 0.1350 sec
[166/200][25/32] 	Loss_D: -0.0688 | Loss_G: -0.4862 | Iteration Time: 0.1400 sec
Training Progress:  84%|████████▎ | 167/200 [16:05<02:51,  5.21s/it]
[166/200][30/32] 	Loss_D: -0.0791 | Loss_G: -0.4725 | Iteration Time: 0.1385 sec
[167/200][0/32] 	Loss_D: -0.0550 | Loss_G: -0.4765 | Iteration Time: 0.1370 sec
[167/200][5/32] 	Loss_D: -0.0698 | Loss_G: -0.4260 | Iteration Time: 0.1355 sec
[167/200][10/32] 	Loss_D: -0.0672 | Loss_G: -0.4788 | Iteration Time: 0.1485 sec
[167/200][15/32] 	Loss_D: -0.0642 | Loss_G: -0.4455 | Iteration Time: 0.1380 sec
[167/200][20/32] 	Loss_D: -0.0776 | Loss_G: -0.4263 | Iteration Time: 0.1450 sec
[167/200][25/32] 	Loss_D: -0.0600 | Loss_G: -0.4986 | Iteration Time: 0.1455 sec
Training Progress:  84%|████████▍ | 168/200 [16:11<02:49,  5.29s/it]
[167/200][30/32] 	Loss_D: -0.0849 | Loss_G: -0.4623 | Iteration Time: 0.1446 sec
[168/200][0/32] 	Loss_D: -0.0784 | Loss_G: -0.4374 | Iteration Time: 0.1490 sec
[168/200][5/32] 	Loss_D: -0.0568 | Loss_G: -0.4870 | Iteration Time: 0.1465 sec
[168/200][10/32] 	Loss_D: -0.0477 | Loss_G: -0.4858 | Iteration Time: 0.1445 sec
[168/200][15/32] 	Loss_D: -0.0645 | Loss_G: -0.4846 | Iteration Time: 0.1445 sec
[168/200][20/32] 	Loss_D: -0.0776 | Loss_G: -0.4722 | Iteration Time: 0.1460 sec
[168/200][25/32] 	Loss_D: -0.0695 | Loss_G: -0.4792 | Iteration Time: 0.1460 sec
Training Progress:  84%|████████▍ | 169/200 [16:17<02:47,  5.40s/it]
[168/200][30/32] 	Loss_D: -0.0777 | Loss_G: -0.4419 | Iteration Time: 0.1460 sec
[169/200][0/32] 	Loss_D: -0.0834 | Loss_G: -0.4597 | Iteration Time: 0.1555 sec
[169/200][5/32] 	Loss_D: -0.0724 | Loss_G: -0.4297 | Iteration Time: 0.1450 sec
[169/200][10/32] 	Loss_D: -0.0781 | Loss_G: -0.4758 | Iteration Time: 0.1445 sec
[169/200][15/32] 	Loss_D: -0.0736 | Loss_G: -0.4942 | Iteration Time: 0.1425 sec
[169/200][20/32] 	Loss_D: -0.0774 | Loss_G: -0.4801 | Iteration Time: 0.1435 sec
[169/200][25/32] 	Loss_D: -0.0680 | Loss_G: -0.4266 | Iteration Time: 0.1505 sec
Training Progress:  85%|████████▌ | 170/200 [16:22<02:43,  5.45s/it]
[169/200][30/32] 	Loss_D: -0.0520 | Loss_G: -0.4780 | Iteration Time: 0.1435 sec
[170/200][0/32] 	Loss_D: -0.0799 | Loss_G: -0.4855 | Iteration Time: 0.1520 sec
[170/200][5/32] 	Loss_D: -0.0835 | Loss_G: -0.4641 | Iteration Time: 0.1445 sec
[170/200][10/32] 	Loss_D: -0.0501 | Loss_G: -0.4881 | Iteration Time: 0.1450 sec
[170/200][15/32] 	Loss_D: -0.0769 | Loss_G: -0.4800 | Iteration Time: 0.1445 sec
[170/200][20/32] 	Loss_D: -0.0863 | Loss_G: -0.4655 | Iteration Time: 0.1425 sec
[170/200][25/32] 	Loss_D: -0.0798 | Loss_G: -0.4321 | Iteration Time: 0.1485 sec
Training Progress:  86%|████████▌ | 171/200 [16:28<02:39,  5.49s/it]
[170/200][30/32] 	Loss_D: -0.0606 | Loss_G: -0.4620 | Iteration Time: 0.1420 sec
[171/200][0/32] 	Loss_D: -0.0787 | Loss_G: -0.4579 | Iteration Time: 0.1540 sec
[171/200][5/32] 	Loss_D: -0.0739 | Loss_G: -0.4286 | Iteration Time: 0.1475 sec
[171/200][10/32] 	Loss_D: -0.0768 | Loss_G: -0.4460 | Iteration Time: 0.1390 sec
[171/200][15/32] 	Loss_D: -0.0815 | Loss_G: -0.4408 | Iteration Time: 0.1331 sec
[171/200][20/32] 	Loss_D: -0.0659 | Loss_G: -0.4956 | Iteration Time: 0.1425 sec
[171/200][25/32] 	Loss_D: -0.0492 | Loss_G: -0.4866 | Iteration Time: 0.1590 sec
Current scores at iteration 5500 | FID: 163.41358947753906 | IS: 2.4548110961914062
No description has been provided for this image
Training Progress:  86%|████████▌ | 172/200 [16:39<03:22,  7.22s/it]
[171/200][30/32] 	Loss_D: -0.0719 | Loss_G: -0.4825 | Iteration Time: 0.1410 sec
[172/200][0/32] 	Loss_D: -0.0751 | Loss_G: -0.4671 | Iteration Time: 0.1610 sec
[172/200][5/32] 	Loss_D: -0.0884 | Loss_G: -0.4438 | Iteration Time: 0.1395 sec
[172/200][10/32] 	Loss_D: -0.0624 | Loss_G: -0.4389 | Iteration Time: 0.1545 sec
[172/200][15/32] 	Loss_D: -0.0796 | Loss_G: -0.4829 | Iteration Time: 0.1440 sec
[172/200][20/32] 	Loss_D: -0.0840 | Loss_G: -0.4323 | Iteration Time: 0.1495 sec
[172/200][25/32] 	Loss_D: -0.0761 | Loss_G: -0.4910 | Iteration Time: 0.1470 sec
Training Progress:  86%|████████▋ | 173/200 [16:45<03:02,  6.75s/it]
[172/200][30/32] 	Loss_D: -0.0558 | Loss_G: -0.4840 | Iteration Time: 0.1445 sec
[173/200][0/32] 	Loss_D: -0.0808 | Loss_G: -0.4405 | Iteration Time: 0.1510 sec
[173/200][5/32] 	Loss_D: -0.0731 | Loss_G: -0.4358 | Iteration Time: 0.1455 sec
[173/200][10/32] 	Loss_D: -0.0600 | Loss_G: -0.4389 | Iteration Time: 0.1435 sec
[173/200][15/32] 	Loss_D: -0.0821 | Loss_G: -0.4912 | Iteration Time: 0.1445 sec
[173/200][20/32] 	Loss_D: -0.0706 | Loss_G: -0.4747 | Iteration Time: 0.1460 sec
[173/200][25/32] 	Loss_D: -0.0714 | Loss_G: -0.4348 | Iteration Time: 0.1445 sec
Training Progress:  87%|████████▋ | 174/200 [16:50<02:46,  6.41s/it]
[173/200][30/32] 	Loss_D: -0.0729 | Loss_G: -0.4328 | Iteration Time: 0.1430 sec
[174/200][0/32] 	Loss_D: -0.0685 | Loss_G: -0.4841 | Iteration Time: 0.1490 sec
[174/200][5/32] 	Loss_D: -0.0770 | Loss_G: -0.4629 | Iteration Time: 0.1460 sec
[174/200][10/32] 	Loss_D: -0.0750 | Loss_G: -0.4305 | Iteration Time: 0.1395 sec
[174/200][15/32] 	Loss_D: -0.0758 | Loss_G: -0.4832 | Iteration Time: 0.1460 sec
[174/200][20/32] 	Loss_D: -0.0672 | Loss_G: -0.4928 | Iteration Time: 0.1475 sec
[174/200][25/32] 	Loss_D: -0.0702 | Loss_G: -0.4816 | Iteration Time: 0.1440 sec
Training Progress:  88%|████████▊ | 175/200 [16:56<02:34,  6.18s/it]
[174/200][30/32] 	Loss_D: -0.0755 | Loss_G: -0.4867 | Iteration Time: 0.1470 sec
[175/200][0/32] 	Loss_D: -0.0846 | Loss_G: -0.4298 | Iteration Time: 0.1500 sec
[175/200][5/32] 	Loss_D: -0.0790 | Loss_G: -0.4801 | Iteration Time: 0.1460 sec
[175/200][10/32] 	Loss_D: -0.0714 | Loss_G: -0.4918 | Iteration Time: 0.1445 sec
[175/200][15/32] 	Loss_D: -0.0681 | Loss_G: -0.4837 | Iteration Time: 0.1505 sec
[175/200][20/32] 	Loss_D: -0.0717 | Loss_G: -0.4868 | Iteration Time: 0.1435 sec
[175/200][25/32] 	Loss_D: -0.0629 | Loss_G: -0.4355 | Iteration Time: 0.1440 sec
Training Progress:  88%|████████▊ | 176/200 [17:02<02:23,  6.00s/it]
[175/200][30/32] 	Loss_D: -0.0600 | Loss_G: -0.4375 | Iteration Time: 0.1440 sec
[176/200][0/32] 	Loss_D: -0.0763 | Loss_G: -0.4830 | Iteration Time: 0.1480 sec
[176/200][5/32] 	Loss_D: -0.0775 | Loss_G: -0.4937 | Iteration Time: 0.1475 sec
[176/200][10/32] 	Loss_D: -0.0797 | Loss_G: -0.4716 | Iteration Time: 0.1440 sec
[176/200][15/32] 	Loss_D: -0.0800 | Loss_G: -0.4703 | Iteration Time: 0.1445 sec
[176/200][20/32] 	Loss_D: -0.0757 | Loss_G: -0.4268 | Iteration Time: 0.1440 sec
[176/200][25/32] 	Loss_D: -0.0563 | Loss_G: -0.4295 | Iteration Time: 0.1350 sec
Training Progress:  88%|████████▊ | 177/200 [17:07<02:14,  5.84s/it]
[176/200][30/32] 	Loss_D: -0.0779 | Loss_G: -0.4434 | Iteration Time: 0.1340 sec
[177/200][0/32] 	Loss_D: -0.0853 | Loss_G: -0.4484 | Iteration Time: 0.1430 sec
[177/200][5/32] 	Loss_D: -0.0708 | Loss_G: -0.4874 | Iteration Time: 0.1500 sec
[177/200][10/32] 	Loss_D: -0.0734 | Loss_G: -0.4869 | Iteration Time: 0.1420 sec
[177/200][15/32] 	Loss_D: -0.0571 | Loss_G: -0.4407 | Iteration Time: 0.1465 sec
[177/200][20/32] 	Loss_D: -0.0715 | Loss_G: -0.4350 | Iteration Time: 0.1500 sec
[177/200][25/32] 	Loss_D: -0.0726 | Loss_G: -0.4736 | Iteration Time: 0.1515 sec
Training Progress:  89%|████████▉ | 178/200 [17:12<02:05,  5.72s/it]
[177/200][30/32] 	Loss_D: -0.0684 | Loss_G: -0.4765 | Iteration Time: 0.0990 sec
[178/200][0/32] 	Loss_D: -0.0652 | Loss_G: -0.4274 | Iteration Time: 0.0935 sec
[178/200][5/32] 	Loss_D: -0.0780 | Loss_G: -0.4707 | Iteration Time: 0.0980 sec
[178/200][10/32] 	Loss_D: -0.0780 | Loss_G: -0.4664 | Iteration Time: 0.0985 sec
[178/200][15/32] 	Loss_D: -0.0690 | Loss_G: -0.4391 | Iteration Time: 0.1145 sec
[178/200][20/32] 	Loss_D: -0.0848 | Loss_G: -0.4440 | Iteration Time: 0.1335 sec
[178/200][25/32] 	Loss_D: -0.0613 | Loss_G: -0.4859 | Iteration Time: 0.1450 sec
Training Progress:  90%|████████▉ | 179/200 [17:17<01:54,  5.45s/it]
[178/200][30/32] 	Loss_D: -0.0716 | Loss_G: -0.4773 | Iteration Time: 0.1440 sec
[179/200][0/32] 	Loss_D: -0.0785 | Loss_G: -0.4867 | Iteration Time: 0.1526 sec
[179/200][5/32] 	Loss_D: -0.0744 | Loss_G: -0.4785 | Iteration Time: 0.1460 sec
[179/200][10/32] 	Loss_D: -0.0697 | Loss_G: -0.4324 | Iteration Time: 0.1445 sec
[179/200][15/32] 	Loss_D: -0.0674 | Loss_G: -0.4815 | Iteration Time: 0.1395 sec
[179/200][20/32] 	Loss_D: -0.0759 | Loss_G: -0.4769 | Iteration Time: 0.1405 sec
[179/200][25/32] 	Loss_D: -0.0732 | Loss_G: -0.4760 | Iteration Time: 0.1405 sec
Training Progress:  90%|█████████ | 180/200 [17:23<01:50,  5.50s/it]
[179/200][30/32] 	Loss_D: -0.0669 | Loss_G: -0.4277 | Iteration Time: 0.1465 sec
[180/200][0/32] 	Loss_D: -0.0889 | Loss_G: -0.4718 | Iteration Time: 0.1445 sec
[180/200][5/32] 	Loss_D: -0.0784 | Loss_G: -0.4646 | Iteration Time: 0.1385 sec
[180/200][10/32] 	Loss_D: -0.0790 | Loss_G: -0.4331 | Iteration Time: 0.1430 sec
[180/200][15/32] 	Loss_D: -0.0710 | Loss_G: -0.4775 | Iteration Time: 0.1375 sec
[180/200][20/32] 	Loss_D: -0.0875 | Loss_G: -0.4247 | Iteration Time: 0.1360 sec
[180/200][25/32] 	Loss_D: -0.0746 | Loss_G: -0.4387 | Iteration Time: 0.1410 sec
Training Progress:  90%|█████████ | 181/200 [17:28<01:44,  5.48s/it]
[180/200][30/32] 	Loss_D: -0.0820 | Loss_G: -0.4467 | Iteration Time: 0.1365 sec
[181/200][0/32] 	Loss_D: -0.0747 | Loss_G: -0.4312 | Iteration Time: 0.1450 sec
[181/200][5/32] 	Loss_D: -0.0706 | Loss_G: -0.4783 | Iteration Time: 0.1425 sec
[181/200][10/32] 	Loss_D: -0.0637 | Loss_G: -0.4332 | Iteration Time: 0.1390 sec
[181/200][15/32] 	Loss_D: -0.0591 | Loss_G: -0.4866 | Iteration Time: 0.1350 sec
[181/200][20/32] 	Loss_D: -0.0732 | Loss_G: -0.4429 | Iteration Time: 0.1400 sec
[181/200][25/32] 	Loss_D: -0.0733 | Loss_G: -0.4795 | Iteration Time: 0.1580 sec
Training Progress:  91%|█████████ | 182/200 [17:34<01:38,  5.49s/it]
[181/200][30/32] 	Loss_D: -0.0689 | Loss_G: -0.4378 | Iteration Time: 0.1530 sec
[182/200][0/32] 	Loss_D: -0.0647 | Loss_G: -0.4814 | Iteration Time: 0.1670 sec
[182/200][5/32] 	Loss_D: -0.0719 | Loss_G: -0.4839 | Iteration Time: 0.1460 sec
[182/200][10/32] 	Loss_D: -0.0737 | Loss_G: -0.4699 | Iteration Time: 0.1480 sec
[182/200][15/32] 	Loss_D: -0.0645 | Loss_G: -0.4885 | Iteration Time: 0.1475 sec
[182/200][20/32] 	Loss_D: -0.0850 | Loss_G: -0.4587 | Iteration Time: 0.1495 sec
[182/200][25/32] 	Loss_D: -0.0830 | Loss_G: -0.4705 | Iteration Time: 0.1455 sec
Training Progress:  92%|█████████▏| 183/200 [17:40<01:34,  5.55s/it]
[182/200][30/32] 	Loss_D: -0.0816 | Loss_G: -0.4630 | Iteration Time: 0.1455 sec
[183/200][0/32] 	Loss_D: -0.0669 | Loss_G: -0.4594 | Iteration Time: 0.1505 sec
[183/200][5/32] 	Loss_D: -0.0685 | Loss_G: -0.4375 | Iteration Time: 0.1475 sec
[183/200][10/32] 	Loss_D: -0.0933 | Loss_G: -0.4501 | Iteration Time: 0.1450 sec
[183/200][15/32] 	Loss_D: -0.0624 | Loss_G: -0.4672 | Iteration Time: 0.1445 sec
[183/200][20/32] 	Loss_D: -0.0499 | Loss_G: -0.4736 | Iteration Time: 0.1430 sec
[183/200][25/32] 	Loss_D: -0.0853 | Loss_G: -0.4341 | Iteration Time: 0.1455 sec
Training Progress:  92%|█████████▏| 184/200 [17:45<01:29,  5.57s/it]
[183/200][30/32] 	Loss_D: -0.0635 | Loss_G: -0.4820 | Iteration Time: 0.1450 sec
[184/200][0/32] 	Loss_D: -0.0767 | Loss_G: -0.4779 | Iteration Time: 0.1525 sec
[184/200][5/32] 	Loss_D: -0.0743 | Loss_G: -0.4833 | Iteration Time: 0.1433 sec
[184/200][10/32] 	Loss_D: -0.0832 | Loss_G: -0.4577 | Iteration Time: 0.1455 sec
[184/200][15/32] 	Loss_D: -0.0614 | Loss_G: -0.4296 | Iteration Time: 0.1450 sec
[184/200][20/32] 	Loss_D: -0.0677 | Loss_G: -0.4871 | Iteration Time: 0.1435 sec
[184/200][25/32] 	Loss_D: -0.0800 | Loss_G: -0.4779 | Iteration Time: 0.1515 sec
Training Progress:  92%|█████████▎| 185/200 [17:51<01:23,  5.59s/it]
[184/200][30/32] 	Loss_D: -0.0732 | Loss_G: -0.4901 | Iteration Time: 0.1445 sec
[185/200][0/32] 	Loss_D: -0.0733 | Loss_G: -0.4700 | Iteration Time: 0.1575 sec
[185/200][5/32] 	Loss_D: -0.0793 | Loss_G: -0.4292 | Iteration Time: 0.1425 sec
[185/200][10/32] 	Loss_D: -0.0800 | Loss_G: -0.4421 | Iteration Time: 0.1465 sec
[185/200][15/32] 	Loss_D: -0.0682 | Loss_G: -0.4260 | Iteration Time: 0.1430 sec
[185/200][20/32] 	Loss_D: -0.0563 | Loss_G: -0.5028 | Iteration Time: 0.1415 sec
[185/200][25/32] 	Loss_D: -0.0706 | Loss_G: -0.4330 | Iteration Time: 0.1460 sec
Training Progress:  93%|█████████▎| 186/200 [17:56<01:18,  5.60s/it]
[185/200][30/32] 	Loss_D: -0.0689 | Loss_G: -0.4725 | Iteration Time: 0.1450 sec
[186/200][0/32] 	Loss_D: -0.0733 | Loss_G: -0.4638 | Iteration Time: 0.1775 sec
[186/200][5/32] 	Loss_D: -0.0877 | Loss_G: -0.4368 | Iteration Time: 0.1450 sec
[186/200][10/32] 	Loss_D: -0.0644 | Loss_G: -0.4866 | Iteration Time: 0.1450 sec
[186/200][15/32] 	Loss_D: -0.0737 | Loss_G: -0.4342 | Iteration Time: 0.1455 sec
[186/200][20/32] 	Loss_D: -0.0712 | Loss_G: -0.4643 | Iteration Time: 0.1425 sec
[186/200][25/32] 	Loss_D: -0.0773 | Loss_G: -0.4530 | Iteration Time: 0.1435 sec
Training Progress:  94%|█████████▎| 187/200 [18:02<01:13,  5.63s/it]
[186/200][30/32] 	Loss_D: -0.0629 | Loss_G: -0.4816 | Iteration Time: 0.1470 sec
[187/200][0/32] 	Loss_D: -0.0649 | Loss_G: -0.4839 | Iteration Time: 0.1535 sec
[187/200][5/32] 	Loss_D: -0.0689 | Loss_G: -0.4823 | Iteration Time: 0.1495 sec
[187/200][10/32] 	Loss_D: -0.0822 | Loss_G: -0.4817 | Iteration Time: 0.1478 sec
[187/200][15/32] 	Loss_D: -0.0871 | Loss_G: -0.4597 | Iteration Time: 0.1475 sec
Current scores at iteration 6000 | FID: 160.5990753173828 | IS: 2.26871919631958
No description has been provided for this image
[187/200][20/32] 	Loss_D: -0.0642 | Loss_G: -0.4861 | Iteration Time: 0.1440 sec
[187/200][25/32] 	Loss_D: -0.0662 | Loss_G: -0.4813 | Iteration Time: 0.1495 sec
Training Progress:  94%|█████████▍| 188/200 [18:13<01:26,  7.21s/it]
[187/200][30/32] 	Loss_D: -0.0729 | Loss_G: -0.4325 | Iteration Time: 0.1485 sec
[188/200][0/32] 	Loss_D: -0.0573 | Loss_G: -0.4760 | Iteration Time: 0.1520 sec
[188/200][5/32] 	Loss_D: -0.0830 | Loss_G: -0.4532 | Iteration Time: 0.1460 sec
[188/200][10/32] 	Loss_D: -0.0729 | Loss_G: -0.4864 | Iteration Time: 0.1440 sec
[188/200][15/32] 	Loss_D: -0.0664 | Loss_G: -0.4907 | Iteration Time: 0.1490 sec
[188/200][20/32] 	Loss_D: -0.0739 | Loss_G: -0.4338 | Iteration Time: 0.1515 sec
[188/200][25/32] 	Loss_D: -0.0575 | Loss_G: -0.4812 | Iteration Time: 0.1330 sec
Training Progress:  94%|█████████▍| 189/200 [18:19<01:13,  6.70s/it]
[188/200][30/32] 	Loss_D: -0.0649 | Loss_G: -0.4932 | Iteration Time: 0.1315 sec
[189/200][0/32] 	Loss_D: -0.0733 | Loss_G: -0.4801 | Iteration Time: 0.1370 sec
[189/200][5/32] 	Loss_D: -0.0931 | Loss_G: -0.4529 | Iteration Time: 0.1460 sec
[189/200][10/32] 	Loss_D: -0.0850 | Loss_G: -0.4699 | Iteration Time: 0.1405 sec
[189/200][15/32] 	Loss_D: -0.0660 | Loss_G: -0.4306 | Iteration Time: 0.1455 sec
[189/200][20/32] 	Loss_D: -0.0819 | Loss_G: -0.4639 | Iteration Time: 0.1495 sec
[189/200][25/32] 	Loss_D: -0.0802 | Loss_G: -0.4630 | Iteration Time: 0.1340 sec
Training Progress:  95%|█████████▌| 190/200 [18:24<01:03,  6.33s/it]
[189/200][30/32] 	Loss_D: -0.0614 | Loss_G: -0.4956 | Iteration Time: 0.1480 sec
[190/200][0/32] 	Loss_D: -0.0742 | Loss_G: -0.4937 | Iteration Time: 0.1425 sec
[190/200][5/32] 	Loss_D: -0.0574 | Loss_G: -0.4843 | Iteration Time: 0.1340 sec
[190/200][10/32] 	Loss_D: -0.0783 | Loss_G: -0.4483 | Iteration Time: 0.1335 sec
[190/200][15/32] 	Loss_D: -0.0798 | Loss_G: -0.4322 | Iteration Time: 0.1430 sec
[190/200][20/32] 	Loss_D: -0.0767 | Loss_G: -0.4718 | Iteration Time: 0.1385 sec
[190/200][25/32] 	Loss_D: -0.0825 | Loss_G: -0.4509 | Iteration Time: 0.1355 sec
Training Progress:  96%|█████████▌| 191/200 [18:29<00:54,  6.03s/it]
[190/200][30/32] 	Loss_D: -0.0803 | Loss_G: -0.4289 | Iteration Time: 0.1308 sec
[191/200][0/32] 	Loss_D: -0.0735 | Loss_G: -0.4824 | Iteration Time: 0.1390 sec
[191/200][5/32] 	Loss_D: -0.0884 | Loss_G: -0.4541 | Iteration Time: 0.1334 sec
[191/200][10/32] 	Loss_D: -0.0804 | Loss_G: -0.4611 | Iteration Time: 0.1342 sec
[191/200][15/32] 	Loss_D: -0.0647 | Loss_G: -0.4334 | Iteration Time: 0.1357 sec
[191/200][20/32] 	Loss_D: -0.0698 | Loss_G: -0.4852 | Iteration Time: 0.1342 sec
[191/200][25/32] 	Loss_D: -0.0625 | Loss_G: -0.4280 | Iteration Time: 0.1340 sec
Training Progress:  96%|█████████▌| 192/200 [18:34<00:46,  5.77s/it]
[191/200][30/32] 	Loss_D: -0.0566 | Loss_G: -0.4909 | Iteration Time: 0.1313 sec
[192/200][0/32] 	Loss_D: -0.0821 | Loss_G: -0.4362 | Iteration Time: 0.1385 sec
[192/200][5/32] 	Loss_D: -0.0750 | Loss_G: -0.4739 | Iteration Time: 0.1310 sec
[192/200][10/32] 	Loss_D: -0.0695 | Loss_G: -0.4698 | Iteration Time: 0.1355 sec
[192/200][15/32] 	Loss_D: -0.0579 | Loss_G: -0.4887 | Iteration Time: 0.1360 sec
[192/200][20/32] 	Loss_D: -0.0707 | Loss_G: -0.4885 | Iteration Time: 0.1375 sec
[192/200][25/32] 	Loss_D: -0.0743 | Loss_G: -0.4344 | Iteration Time: 0.1380 sec
Training Progress:  96%|█████████▋| 193/200 [18:40<00:39,  5.61s/it]
[192/200][30/32] 	Loss_D: -0.0629 | Loss_G: -0.4834 | Iteration Time: 0.1365 sec
[193/200][0/32] 	Loss_D: -0.0769 | Loss_G: -0.4801 | Iteration Time: 0.1395 sec
[193/200][5/32] 	Loss_D: -0.0734 | Loss_G: -0.4306 | Iteration Time: 0.1375 sec
[193/200][10/32] 	Loss_D: -0.0796 | Loss_G: -0.4590 | Iteration Time: 0.1315 sec
[193/200][15/32] 	Loss_D: -0.0732 | Loss_G: -0.4788 | Iteration Time: 0.1315 sec
[193/200][20/32] 	Loss_D: -0.0764 | Loss_G: -0.4405 | Iteration Time: 0.1335 sec
[193/200][25/32] 	Loss_D: -0.0649 | Loss_G: -0.4376 | Iteration Time: 0.1295 sec
Training Progress:  97%|█████████▋| 194/200 [18:45<00:33,  5.50s/it]
[193/200][30/32] 	Loss_D: -0.0732 | Loss_G: -0.4440 | Iteration Time: 0.1325 sec
[194/200][0/32] 	Loss_D: -0.0674 | Loss_G: -0.4900 | Iteration Time: 0.1377 sec
[194/200][5/32] 	Loss_D: -0.0705 | Loss_G: -0.4766 | Iteration Time: 0.1340 sec
[194/200][10/32] 	Loss_D: -0.0615 | Loss_G: -0.4826 | Iteration Time: 0.1337 sec
[194/200][15/32] 	Loss_D: -0.0684 | Loss_G: -0.4377 | Iteration Time: 0.1350 sec
[194/200][20/32] 	Loss_D: -0.0766 | Loss_G: -0.4667 | Iteration Time: 0.1330 sec
[194/200][25/32] 	Loss_D: -0.0604 | Loss_G: -0.4992 | Iteration Time: 0.1356 sec
Training Progress:  98%|█████████▊| 195/200 [18:50<00:27,  5.41s/it]
[194/200][30/32] 	Loss_D: -0.0804 | Loss_G: -0.4414 | Iteration Time: 0.1320 sec
[195/200][0/32] 	Loss_D: -0.0624 | Loss_G: -0.4914 | Iteration Time: 0.1400 sec
[195/200][5/32] 	Loss_D: -0.0642 | Loss_G: -0.4858 | Iteration Time: 0.1340 sec
[195/200][10/32] 	Loss_D: -0.0864 | Loss_G: -0.4565 | Iteration Time: 0.1350 sec
[195/200][15/32] 	Loss_D: -0.0790 | Loss_G: -0.4502 | Iteration Time: 0.1340 sec
[195/200][20/32] 	Loss_D: -0.0660 | Loss_G: -0.4834 | Iteration Time: 0.1310 sec
[195/200][25/32] 	Loss_D: -0.0572 | Loss_G: -0.4919 | Iteration Time: 0.1295 sec
Training Progress:  98%|█████████▊| 196/200 [18:55<00:21,  5.33s/it]
[195/200][30/32] 	Loss_D: -0.0682 | Loss_G: -0.4869 | Iteration Time: 0.1303 sec
[196/200][0/32] 	Loss_D: -0.0740 | Loss_G: -0.4433 | Iteration Time: 0.1335 sec
[196/200][5/32] 	Loss_D: -0.0809 | Loss_G: -0.4517 | Iteration Time: 0.1320 sec
[196/200][10/32] 	Loss_D: -0.0769 | Loss_G: -0.4931 | Iteration Time: 0.1356 sec
[196/200][15/32] 	Loss_D: -0.0804 | Loss_G: -0.4270 | Iteration Time: 0.1360 sec
[196/200][20/32] 	Loss_D: -0.0831 | Loss_G: -0.4540 | Iteration Time: 0.1345 sec
[196/200][25/32] 	Loss_D: -0.0759 | Loss_G: -0.4463 | Iteration Time: 0.1335 sec
Training Progress:  98%|█████████▊| 197/200 [19:00<00:15,  5.27s/it]
[196/200][30/32] 	Loss_D: -0.0728 | Loss_G: -0.4914 | Iteration Time: 0.1355 sec
[197/200][0/32] 	Loss_D: -0.0749 | Loss_G: -0.4364 | Iteration Time: 0.1425 sec
[197/200][5/32] 	Loss_D: -0.0858 | Loss_G: -0.4688 | Iteration Time: 0.1328 sec
[197/200][10/32] 	Loss_D: -0.0840 | Loss_G: -0.4466 | Iteration Time: 0.1305 sec
[197/200][15/32] 	Loss_D: -0.0770 | Loss_G: -0.4465 | Iteration Time: 0.1319 sec
[197/200][20/32] 	Loss_D: -0.0700 | Loss_G: -0.4857 | Iteration Time: 0.1315 sec
[197/200][25/32] 	Loss_D: -0.0758 | Loss_G: -0.4734 | Iteration Time: 0.1350 sec
Training Progress:  99%|█████████▉| 198/200 [19:06<00:10,  5.25s/it]
[197/200][30/32] 	Loss_D: -0.0669 | Loss_G: -0.4321 | Iteration Time: 0.1334 sec
[198/200][0/32] 	Loss_D: -0.0751 | Loss_G: -0.4819 | Iteration Time: 0.1500 sec
[198/200][5/32] 	Loss_D: -0.0654 | Loss_G: -0.4884 | Iteration Time: 0.1320 sec
[198/200][10/32] 	Loss_D: -0.0667 | Loss_G: -0.4256 | Iteration Time: 0.1304 sec
[198/200][15/32] 	Loss_D: -0.0739 | Loss_G: -0.4502 | Iteration Time: 0.1330 sec
[198/200][20/32] 	Loss_D: -0.0845 | Loss_G: -0.4466 | Iteration Time: 0.1320 sec
[198/200][25/32] 	Loss_D: -0.0647 | Loss_G: -0.4835 | Iteration Time: 0.1320 sec
Training Progress: 100%|█████████▉| 199/200 [19:11<00:05,  5.22s/it]
[198/200][30/32] 	Loss_D: -0.0796 | Loss_G: -0.4674 | Iteration Time: 0.1341 sec
[199/200][0/32] 	Loss_D: -0.0699 | Loss_G: -0.4888 | Iteration Time: 0.1445 sec
[199/200][5/32] 	Loss_D: -0.0697 | Loss_G: -0.4792 | Iteration Time: 0.1305 sec
[199/200][10/32] 	Loss_D: -0.0606 | Loss_G: -0.4890 | Iteration Time: 0.1310 sec
[199/200][15/32] 	Loss_D: -0.0678 | Loss_G: -0.4326 | Iteration Time: 0.1290 sec
[199/200][20/32] 	Loss_D: -0.0640 | Loss_G: -0.4932 | Iteration Time: 0.1355 sec
[199/200][25/32] 	Loss_D: -0.0550 | Loss_G: -0.4324 | Iteration Time: 0.1330 sec
[199/200][30/32] 	Loss_D: -0.0712 | Loss_G: -0.4888 | Iteration Time: 0.1307 sec
Current scores at iteration 6399 | FID: 209.2201385498047 | IS: 1.5033737421035767
No description has been provided for this image
Training Progress: 100%|██████████| 200/200 [19:19<00:00,  5.80s/it]

LOSS PLOT¶

In [93]:
wgan_trainer.plot_loss()
No description has been provided for this image

SCORE PLOT¶

In [94]:
wgan_trainer.plot_scores()
Minimum FID Score of 160.5990753173828 obtained at iteration of 6000.
Maximum IS Score of 2.464463710784912.
No description has been provided for this image

WGAN-GP¶

GENERATOR¶

In [69]:
# Create generator
netG = WGANGPGenerator().to(device=device)
# Print generator
print(netG)
WGANGPGenerator(
  (model): Sequential(
    (0): Sequential(
      (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (1): Sequential(
      (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (2): Sequential(
      (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (3): Sequential(
      (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.2)
    )
    (4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (5): Tanh()
  )
)

CRITIC¶

In [70]:
# Create Critic
critic = WGANGPCritic().to(device=device)
# Print critic
print(critic)
WGANGPCritic(
  (model): Sequential(
    (0): Sequential(
      (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (1): ReLU(inplace=True)
    )
    (1): Sequential(
      (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): InstanceNorm2d(256, eps=1e-05, momentum=0.9, affine=False, track_running_stats=False)
      (2): ReLU(inplace=True)
    )
    (2): Sequential(
      (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): InstanceNorm2d(512, eps=1e-05, momentum=0.9, affine=False, track_running_stats=False)
      (2): ReLU(inplace=True)
    )
    (3): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (4): Sigmoid()
  )
)

OPTIMIZERS AND HYPERPARAMETERS¶

In [71]:
# Optimizers
optimizerC = optim.Adam(critic.parameters(), lr=LR, betas=(0.0, 0.9)) 
optimizerG = optim.Adam(netG.parameters(), lr=LR, betas=(0.0, 0.9))
# Fixed noise (latent vectors)
fixed_noise = torch.randn(IMAGE_SIZE, nz, 1, 1, device=device)
# Lambda
lambda_gp = 10

TRAINING¶

In [72]:
wgangp_trainer = WGANGPTrainer(critic, netG, cat_dataloader, device, fixed_noise, real_label, fake_label, nz, optimizerC, optimizerG, EPOCHS, CRITIC_ITERATIONS, lambda_gp)
wgangp_trainer.train()
Starting Training Loop...
Training Progress:   0%|          | 0/200 [00:00<?, ?it/s]
[0/200][0/32] 	Loss_D: 0.3098 | Loss_G: -0.4351 | Iteration Time: 1.6757 sec
Current scores at iteration 0 | FID: 363.68902587890625 | IS: 1.1739404201507568
No description has been provided for this image
[0/200][5/32] 	Loss_D: 0.0249 | Loss_G: -0.2451 | Iteration Time: 1.1456 sec
[0/200][10/32] 	Loss_D: -0.2798 | Loss_G: -0.2202 | Iteration Time: 0.9056 sec
[0/200][15/32] 	Loss_D: 0.3199 | Loss_G: -0.3168 | Iteration Time: 1.1264 sec
[0/200][20/32] 	Loss_D: -0.2145 | Loss_G: -0.2528 | Iteration Time: 0.9751 sec
[0/200][25/32] 	Loss_D: -0.2108 | Loss_G: -0.3601 | Iteration Time: 1.2558 sec
Training Progress:   0%|          | 1/200 [00:43<2:22:58, 43.11s/it]
[0/200][30/32] 	Loss_D: -0.1920 | Loss_G: -0.3887 | Iteration Time: 0.9213 sec
[1/200][0/32] 	Loss_D: -0.0512 | Loss_G: -0.3675 | Iteration Time: 0.9041 sec
[1/200][5/32] 	Loss_D: 0.0098 | Loss_G: -0.3825 | Iteration Time: 1.0291 sec
[1/200][10/32] 	Loss_D: -0.1522 | Loss_G: -0.3452 | Iteration Time: 1.2386 sec
[1/200][15/32] 	Loss_D: -0.2318 | Loss_G: -0.3863 | Iteration Time: 0.9996 sec
[1/200][20/32] 	Loss_D: -0.0175 | Loss_G: -0.3134 | Iteration Time: 0.9616 sec
[1/200][25/32] 	Loss_D: -0.1546 | Loss_G: -0.3689 | Iteration Time: 0.9581 sec
Training Progress:   1%|          | 2/200 [01:16<2:04:04, 37.60s/it]
[1/200][30/32] 	Loss_D: -0.1740 | Loss_G: -0.2294 | Iteration Time: 1.3574 sec
[2/200][0/32] 	Loss_D: -0.2770 | Loss_G: -0.3015 | Iteration Time: 0.9561 sec
[2/200][5/32] 	Loss_D: -0.1375 | Loss_G: -0.2809 | Iteration Time: 1.0056 sec
[2/200][10/32] 	Loss_D: -0.1035 | Loss_G: -0.3684 | Iteration Time: 1.3871 sec
[2/200][15/32] 	Loss_D: 0.0478 | Loss_G: -0.3379 | Iteration Time: 1.4082 sec
[2/200][20/32] 	Loss_D: -0.0658 | Loss_G: -0.3135 | Iteration Time: 0.6466 sec
[2/200][25/32] 	Loss_D: -0.0204 | Loss_G: -0.4139 | Iteration Time: 0.9207 sec
Training Progress:   2%|▏         | 3/200 [01:52<1:59:53, 36.51s/it]
[2/200][30/32] 	Loss_D: -0.1665 | Loss_G: -0.3810 | Iteration Time: 1.2556 sec
[3/200][0/32] 	Loss_D: -0.2015 | Loss_G: -0.3510 | Iteration Time: 0.9406 sec
[3/200][5/32] 	Loss_D: -0.0989 | Loss_G: -0.2984 | Iteration Time: 1.0134 sec
[3/200][10/32] 	Loss_D: -0.1801 | Loss_G: -0.4507 | Iteration Time: 1.3761 sec
[3/200][15/32] 	Loss_D: 0.2196 | Loss_G: -0.2984 | Iteration Time: 1.3361 sec
[3/200][20/32] 	Loss_D: -0.1878 | Loss_G: -0.3951 | Iteration Time: 0.6381 sec
[3/200][25/32] 	Loss_D: -0.0354 | Loss_G: -0.2700 | Iteration Time: 0.9196 sec
Training Progress:   2%|▏         | 4/200 [02:26<1:57:10, 35.87s/it]
[3/200][30/32] 	Loss_D: -0.0964 | Loss_G: -0.3597 | Iteration Time: 1.2347 sec
[4/200][0/32] 	Loss_D: 0.0774 | Loss_G: -0.2425 | Iteration Time: 0.8976 sec
[4/200][5/32] 	Loss_D: -0.1144 | Loss_G: -0.4004 | Iteration Time: 1.0236 sec
[4/200][10/32] 	Loss_D: -0.0220 | Loss_G: -0.2667 | Iteration Time: 1.3866 sec
[4/200][15/32] 	Loss_D: -0.0438 | Loss_G: -0.3837 | Iteration Time: 1.3280 sec
[4/200][20/32] 	Loss_D: -0.1342 | Loss_G: -0.3197 | Iteration Time: 0.6346 sec
[4/200][25/32] 	Loss_D: -0.1326 | Loss_G: -0.3963 | Iteration Time: 0.9021 sec
Training Progress:   2%|▎         | 5/200 [03:01<1:55:09, 35.43s/it]
[4/200][30/32] 	Loss_D: -0.0922 | Loss_G: -0.3720 | Iteration Time: 1.2381 sec
[5/200][0/32] 	Loss_D: -0.1220 | Loss_G: -0.4142 | Iteration Time: 0.9501 sec
[5/200][5/32] 	Loss_D: -0.0134 | Loss_G: -0.4536 | Iteration Time: 1.0036 sec
[5/200][10/32] 	Loss_D: -0.1493 | Loss_G: -0.4380 | Iteration Time: 1.3831 sec
[5/200][15/32] 	Loss_D: -0.1112 | Loss_G: -0.3367 | Iteration Time: 1.3387 sec
[5/200][20/32] 	Loss_D: -0.1664 | Loss_G: -0.5221 | Iteration Time: 0.6471 sec
[5/200][25/32] 	Loss_D: -0.0973 | Loss_G: -0.4194 | Iteration Time: 0.9081 sec
Training Progress:   3%|▎         | 6/200 [03:36<1:54:06, 35.29s/it]
[5/200][30/32] 	Loss_D: -0.1136 | Loss_G: -0.4890 | Iteration Time: 1.2731 sec
[6/200][0/32] 	Loss_D: -0.0771 | Loss_G: -0.4711 | Iteration Time: 0.9710 sec
[6/200][5/32] 	Loss_D: -0.1785 | Loss_G: -0.4337 | Iteration Time: 0.9816 sec
[6/200][10/32] 	Loss_D: -0.1549 | Loss_G: -0.4595 | Iteration Time: 1.3902 sec
[6/200][15/32] 	Loss_D: -0.0855 | Loss_G: -0.5951 | Iteration Time: 1.3377 sec
[6/200][20/32] 	Loss_D: -0.1111 | Loss_G: -0.3803 | Iteration Time: 0.6401 sec
[6/200][25/32] 	Loss_D: -0.1443 | Loss_G: -0.4325 | Iteration Time: 0.9176 sec
Training Progress:   4%|▎         | 7/200 [04:11<1:53:00, 35.13s/it]
[6/200][30/32] 	Loss_D: -0.1148 | Loss_G: -0.4189 | Iteration Time: 1.2586 sec
[7/200][0/32] 	Loss_D: -0.1612 | Loss_G: -0.4112 | Iteration Time: 0.9771 sec
[7/200][5/32] 	Loss_D: -0.0969 | Loss_G: -0.4863 | Iteration Time: 1.0075 sec
[7/200][10/32] 	Loss_D: -0.1229 | Loss_G: -0.4548 | Iteration Time: 1.3907 sec
[7/200][15/32] 	Loss_D: -0.1137 | Loss_G: -0.4576 | Iteration Time: 1.3529 sec
[7/200][20/32] 	Loss_D: -0.0158 | Loss_G: -0.4644 | Iteration Time: 0.6366 sec
[7/200][25/32] 	Loss_D: -0.0823 | Loss_G: -0.4096 | Iteration Time: 0.9306 sec
Training Progress:   4%|▍         | 8/200 [04:46<1:52:28, 35.15s/it]
[7/200][30/32] 	Loss_D: 0.0843 | Loss_G: -0.3684 | Iteration Time: 1.2553 sec
[8/200][0/32] 	Loss_D: -0.1176 | Loss_G: -0.4164 | Iteration Time: 0.9646 sec
[8/200][5/32] 	Loss_D: -0.0800 | Loss_G: -0.5981 | Iteration Time: 1.0146 sec
[8/200][10/32] 	Loss_D: -0.1235 | Loss_G: -0.4388 | Iteration Time: 1.3771 sec
[8/200][15/32] 	Loss_D: -0.0955 | Loss_G: -0.4880 | Iteration Time: 1.3349 sec
[8/200][20/32] 	Loss_D: -0.0860 | Loss_G: -0.4237 | Iteration Time: 0.6381 sec
[8/200][25/32] 	Loss_D: -0.1152 | Loss_G: -0.3863 | Iteration Time: 0.9302 sec
Training Progress:   4%|▍         | 9/200 [05:21<1:51:39, 35.07s/it]
[8/200][30/32] 	Loss_D: -0.1318 | Loss_G: -0.4093 | Iteration Time: 1.2479 sec
[9/200][0/32] 	Loss_D: -0.0857 | Loss_G: -0.4608 | Iteration Time: 0.9651 sec
[9/200][5/32] 	Loss_D: -0.0864 | Loss_G: -0.4807 | Iteration Time: 1.0086 sec
[9/200][10/32] 	Loss_D: -0.0241 | Loss_G: -0.4763 | Iteration Time: 1.3606 sec
[9/200][15/32] 	Loss_D: -0.1166 | Loss_G: -0.4407 | Iteration Time: 1.3293 sec
[9/200][20/32] 	Loss_D: 0.1159 | Loss_G: -0.6701 | Iteration Time: 0.6267 sec
[9/200][25/32] 	Loss_D: -0.0690 | Loss_G: -0.4210 | Iteration Time: 0.8859 sec
Training Progress:   5%|▌         | 10/200 [05:56<1:50:36, 34.93s/it]
[9/200][30/32] 	Loss_D: -0.0946 | Loss_G: -0.3827 | Iteration Time: 1.2314 sec
[10/200][0/32] 	Loss_D: -0.0124 | Loss_G: -0.3112 | Iteration Time: 0.9471 sec
[10/200][5/32] 	Loss_D: 0.0124 | Loss_G: -0.5385 | Iteration Time: 1.0172 sec
[10/200][10/32] 	Loss_D: -0.0951 | Loss_G: -0.4214 | Iteration Time: 1.3752 sec
[10/200][15/32] 	Loss_D: -0.1094 | Loss_G: -0.4464 | Iteration Time: 1.3472 sec
[10/200][20/32] 	Loss_D: -0.0843 | Loss_G: -0.4272 | Iteration Time: 0.6466 sec
[10/200][25/32] 	Loss_D: -0.0340 | Loss_G: -0.4351 | Iteration Time: 0.9111 sec
Training Progress:   6%|▌         | 11/200 [06:31<1:49:59, 34.92s/it]
[10/200][30/32] 	Loss_D: -0.0652 | Loss_G: -0.3638 | Iteration Time: 1.2526 sec
[11/200][0/32] 	Loss_D: -0.0601 | Loss_G: -0.4535 | Iteration Time: 0.9481 sec
[11/200][5/32] 	Loss_D: -0.0823 | Loss_G: -0.4332 | Iteration Time: 1.0561 sec
[11/200][10/32] 	Loss_D: -0.0545 | Loss_G: -0.4387 | Iteration Time: 1.3731 sec
[11/200][15/32] 	Loss_D: -0.0715 | Loss_G: -0.5404 | Iteration Time: 1.3434 sec
[11/200][20/32] 	Loss_D: -0.0811 | Loss_G: -0.3960 | Iteration Time: 0.6426 sec
[11/200][25/32] 	Loss_D: -0.1035 | Loss_G: -0.4209 | Iteration Time: 0.9086 sec
Training Progress:   6%|▌         | 12/200 [07:06<1:49:32, 34.96s/it]
[11/200][30/32] 	Loss_D: -0.0459 | Loss_G: -0.3893 | Iteration Time: 1.2521 sec
[12/200][0/32] 	Loss_D: -0.1138 | Loss_G: -0.4139 | Iteration Time: 0.9771 sec
[12/200][5/32] 	Loss_D: -0.0818 | Loss_G: -0.4387 | Iteration Time: 1.0346 sec
[12/200][10/32] 	Loss_D: -0.0812 | Loss_G: -0.4780 | Iteration Time: 1.3986 sec
[12/200][15/32] 	Loss_D: -0.1237 | Loss_G: -0.4095 | Iteration Time: 1.3644 sec
[12/200][20/32] 	Loss_D: 0.0313 | Loss_G: -0.6228 | Iteration Time: 0.6416 sec
[12/200][25/32] 	Loss_D: -0.0875 | Loss_G: -0.4559 | Iteration Time: 0.9574 sec
Training Progress:   6%|▋         | 13/200 [07:41<1:49:22, 35.09s/it]
[12/200][30/32] 	Loss_D: 0.0121 | Loss_G: -0.5668 | Iteration Time: 1.2731 sec
[13/200][0/32] 	Loss_D: -0.0910 | Loss_G: -0.3128 | Iteration Time: 0.9476 sec
[13/200][5/32] 	Loss_D: -0.0985 | Loss_G: -0.4242 | Iteration Time: 1.0252 sec
[13/200][10/32] 	Loss_D: -0.0353 | Loss_G: -0.1654 | Iteration Time: 1.3841 sec
[13/200][15/32] 	Loss_D: -0.1046 | Loss_G: -0.4235 | Iteration Time: 1.3451 sec
[13/200][20/32] 	Loss_D: -0.0808 | Loss_G: -0.3720 | Iteration Time: 0.6428 sec
[13/200][25/32] 	Loss_D: -0.0295 | Loss_G: -0.4877 | Iteration Time: 0.9198 sec
Training Progress:   7%|▋         | 14/200 [08:16<1:48:40, 35.05s/it]
[13/200][30/32] 	Loss_D: -0.0840 | Loss_G: -0.4613 | Iteration Time: 1.2421 sec
[14/200][0/32] 	Loss_D: -0.0928 | Loss_G: -0.4558 | Iteration Time: 0.9436 sec
[14/200][5/32] 	Loss_D: -0.0977 | Loss_G: -0.4544 | Iteration Time: 1.0060 sec
[14/200][10/32] 	Loss_D: -0.0591 | Loss_G: -0.5080 | Iteration Time: 1.3752 sec
[14/200][15/32] 	Loss_D: -0.0978 | Loss_G: -0.4767 | Iteration Time: 1.3263 sec
[14/200][20/32] 	Loss_D: -0.0672 | Loss_G: -0.5308 | Iteration Time: 0.6411 sec
[14/200][25/32] 	Loss_D: -0.0747 | Loss_G: -0.4503 | Iteration Time: 0.8941 sec
Training Progress:   8%|▊         | 15/200 [08:51<1:47:48, 34.96s/it]
[14/200][30/32] 	Loss_D: -0.0658 | Loss_G: -0.4928 | Iteration Time: 1.2375 sec
[15/200][0/32] 	Loss_D: -0.0907 | Loss_G: -0.4216 | Iteration Time: 0.9046 sec
[15/200][5/32] 	Loss_D: -0.0516 | Loss_G: -0.3966 | Iteration Time: 1.0165 sec
[15/200][10/32] 	Loss_D: -0.0996 | Loss_G: -0.4709 | Iteration Time: 1.3767 sec
[15/200][15/32] 	Loss_D: -0.0689 | Loss_G: -0.5159 | Iteration Time: 1.3411 sec
[15/200][20/32] 	Loss_D: -0.1127 | Loss_G: -0.4399 | Iteration Time: 0.6326 sec
Current scores at iteration 500 | FID: 168.53553771972656 | IS: 2.5484938621520996
No description has been provided for this image
[15/200][25/32] 	Loss_D: -0.0703 | Loss_G: -0.4271 | Iteration Time: 1.0491 sec
Training Progress:   8%|▊         | 16/200 [09:32<1:53:30, 37.01s/it]
[15/200][30/32] 	Loss_D: -0.1149 | Loss_G: -0.4652 | Iteration Time: 1.3072 sec
[16/200][0/32] 	Loss_D: -0.0857 | Loss_G: -0.4771 | Iteration Time: 1.3777 sec
[16/200][5/32] 	Loss_D: -0.0447 | Loss_G: -0.6009 | Iteration Time: 1.4067 sec
[16/200][10/32] 	Loss_D: -0.0962 | Loss_G: -0.4312 | Iteration Time: 1.3918 sec
[16/200][15/32] 	Loss_D: -0.1101 | Loss_G: -0.4426 | Iteration Time: 1.0831 sec
[16/200][20/32] 	Loss_D: -0.0971 | Loss_G: -0.4105 | Iteration Time: 0.9861 sec
[16/200][25/32] 	Loss_D: -0.0847 | Loss_G: -0.3714 | Iteration Time: 1.4057 sec
Training Progress:   8%|▊         | 17/200 [10:08<1:51:14, 36.47s/it]
[16/200][30/32] 	Loss_D: -0.1125 | Loss_G: -0.4568 | Iteration Time: 1.0281 sec
[17/200][0/32] 	Loss_D: -0.0513 | Loss_G: -0.5506 | Iteration Time: 1.3802 sec
[17/200][5/32] 	Loss_D: -0.0833 | Loss_G: -0.5310 | Iteration Time: 1.3899 sec
[17/200][10/32] 	Loss_D: -0.0798 | Loss_G: -0.4724 | Iteration Time: 1.4192 sec
[17/200][15/32] 	Loss_D: -0.1110 | Loss_G: -0.4370 | Iteration Time: 1.0320 sec
[17/200][20/32] 	Loss_D: -0.0837 | Loss_G: -0.4125 | Iteration Time: 1.0191 sec
[17/200][25/32] 	Loss_D: -0.0249 | Loss_G: -0.2358 | Iteration Time: 1.4124 sec
Training Progress:   9%|▉         | 18/200 [10:43<1:49:43, 36.17s/it]
[17/200][30/32] 	Loss_D: -0.0846 | Loss_G: -0.5503 | Iteration Time: 1.0307 sec
[18/200][0/32] 	Loss_D: -0.1126 | Loss_G: -0.4352 | Iteration Time: 1.3712 sec
[18/200][5/32] 	Loss_D: -0.1261 | Loss_G: -0.4844 | Iteration Time: 1.3516 sec
[18/200][10/32] 	Loss_D: -0.0771 | Loss_G: -0.4095 | Iteration Time: 1.3977 sec
[18/200][15/32] 	Loss_D: -0.0669 | Loss_G: -0.2815 | Iteration Time: 1.0341 sec
[18/200][20/32] 	Loss_D: -0.0790 | Loss_G: -0.3343 | Iteration Time: 1.0181 sec
[18/200][25/32] 	Loss_D: -0.0829 | Loss_G: -0.3600 | Iteration Time: 1.4011 sec
Training Progress:  10%|▉         | 19/200 [11:18<1:48:08, 35.85s/it]
[18/200][30/32] 	Loss_D: -0.0697 | Loss_G: -0.3238 | Iteration Time: 1.0122 sec
[19/200][0/32] 	Loss_D: -0.1214 | Loss_G: -0.3958 | Iteration Time: 1.3422 sec
[19/200][5/32] 	Loss_D: -0.0735 | Loss_G: -0.4933 | Iteration Time: 1.4017 sec
[19/200][10/32] 	Loss_D: -0.0532 | Loss_G: -0.3845 | Iteration Time: 1.3911 sec
[19/200][15/32] 	Loss_D: -0.1045 | Loss_G: -0.4483 | Iteration Time: 1.0118 sec
[19/200][20/32] 	Loss_D: -0.1134 | Loss_G: -0.4568 | Iteration Time: 0.9901 sec
[19/200][25/32] 	Loss_D: -0.0757 | Loss_G: -0.4264 | Iteration Time: 1.3806 sec
Training Progress:  10%|█         | 20/200 [11:53<1:46:50, 35.62s/it]
[19/200][30/32] 	Loss_D: -0.0840 | Loss_G: -0.4354 | Iteration Time: 1.0232 sec
[20/200][0/32] 	Loss_D: -0.0836 | Loss_G: -0.3862 | Iteration Time: 1.3501 sec
[20/200][5/32] 	Loss_D: -0.0776 | Loss_G: -0.4016 | Iteration Time: 1.4182 sec
[20/200][10/32] 	Loss_D: -0.0534 | Loss_G: -0.3652 | Iteration Time: 1.3957 sec
[20/200][15/32] 	Loss_D: -0.1097 | Loss_G: -0.3610 | Iteration Time: 1.0284 sec
[20/200][20/32] 	Loss_D: -0.0389 | Loss_G: -0.3986 | Iteration Time: 1.0066 sec
[20/200][25/32] 	Loss_D: -0.0962 | Loss_G: -0.5122 | Iteration Time: 1.3793 sec
Training Progress:  10%|█         | 21/200 [12:28<1:45:43, 35.44s/it]
[20/200][30/32] 	Loss_D: -0.0899 | Loss_G: -0.5555 | Iteration Time: 1.0246 sec
[21/200][0/32] 	Loss_D: -0.0477 | Loss_G: -0.3788 | Iteration Time: 1.3327 sec
[21/200][5/32] 	Loss_D: -0.1133 | Loss_G: -0.4392 | Iteration Time: 1.4302 sec
[21/200][10/32] 	Loss_D: -0.0942 | Loss_G: -0.4255 | Iteration Time: 1.3926 sec
[21/200][15/32] 	Loss_D: -0.1277 | Loss_G: -0.4432 | Iteration Time: 1.0262 sec
[21/200][20/32] 	Loss_D: -0.1334 | Loss_G: -0.4600 | Iteration Time: 0.9986 sec
[21/200][25/32] 	Loss_D: -0.0353 | Loss_G: -0.2876 | Iteration Time: 1.3896 sec
Training Progress:  11%|█         | 22/200 [13:03<1:44:49, 35.33s/it]
[21/200][30/32] 	Loss_D: -0.1033 | Loss_G: -0.4043 | Iteration Time: 1.0108 sec
[22/200][0/32] 	Loss_D: -0.1214 | Loss_G: -0.4062 | Iteration Time: 1.3621 sec
[22/200][5/32] 	Loss_D: -0.1357 | Loss_G: -0.4350 | Iteration Time: 1.4111 sec
[22/200][10/32] 	Loss_D: 0.0370 | Loss_G: -0.3954 | Iteration Time: 1.4196 sec
[22/200][15/32] 	Loss_D: -0.1389 | Loss_G: -0.4476 | Iteration Time: 1.0416 sec
[22/200][20/32] 	Loss_D: -0.1117 | Loss_G: -0.3724 | Iteration Time: 1.0066 sec
[22/200][25/32] 	Loss_D: -0.0311 | Loss_G: -0.4473 | Iteration Time: 1.4086 sec
Training Progress:  12%|█▏        | 23/200 [13:39<1:44:27, 35.41s/it]
[22/200][30/32] 	Loss_D: -0.1296 | Loss_G: -0.4454 | Iteration Time: 1.0227 sec
[23/200][0/32] 	Loss_D: -0.1247 | Loss_G: -0.4381 | Iteration Time: 1.3596 sec
[23/200][5/32] 	Loss_D: -0.1275 | Loss_G: -0.3945 | Iteration Time: 1.4420 sec
[23/200][10/32] 	Loss_D: -0.1031 | Loss_G: -0.4624 | Iteration Time: 1.4193 sec
[23/200][15/32] 	Loss_D: -0.1182 | Loss_G: -0.3833 | Iteration Time: 1.0353 sec
[23/200][20/32] 	Loss_D: -0.1303 | Loss_G: -0.3854 | Iteration Time: 1.0036 sec
[23/200][25/32] 	Loss_D: -0.1266 | Loss_G: -0.4442 | Iteration Time: 1.3457 sec
Training Progress:  12%|█▏        | 24/200 [14:14<1:43:44, 35.36s/it]
[23/200][30/32] 	Loss_D: -0.0822 | Loss_G: -0.3798 | Iteration Time: 1.0197 sec
[24/200][0/32] 	Loss_D: -0.1006 | Loss_G: -0.4286 | Iteration Time: 1.3437 sec
[24/200][5/32] 	Loss_D: -0.0998 | Loss_G: -0.4154 | Iteration Time: 1.3923 sec
[24/200][10/32] 	Loss_D: -0.0932 | Loss_G: -0.3336 | Iteration Time: 1.3706 sec
[24/200][15/32] 	Loss_D: -0.0520 | Loss_G: -0.4326 | Iteration Time: 1.0162 sec
[24/200][20/32] 	Loss_D: -0.1124 | Loss_G: -0.3251 | Iteration Time: 1.0052 sec
[24/200][25/32] 	Loss_D: -0.1101 | Loss_G: -0.4458 | Iteration Time: 1.3792 sec
Training Progress:  12%|█▎        | 25/200 [14:49<1:42:53, 35.27s/it]
[24/200][30/32] 	Loss_D: -0.1307 | Loss_G: -0.3526 | Iteration Time: 1.0316 sec
[25/200][0/32] 	Loss_D: -0.1359 | Loss_G: -0.4661 | Iteration Time: 1.3651 sec
[25/200][5/32] 	Loss_D: -0.0830 | Loss_G: -0.3759 | Iteration Time: 1.3817 sec
[25/200][10/32] 	Loss_D: -0.1253 | Loss_G: -0.4656 | Iteration Time: 1.3937 sec
[25/200][15/32] 	Loss_D: -0.0794 | Loss_G: -0.3542 | Iteration Time: 1.0241 sec
[25/200][20/32] 	Loss_D: -0.1476 | Loss_G: -0.4157 | Iteration Time: 1.0002 sec
[25/200][25/32] 	Loss_D: -0.0747 | Loss_G: -0.4354 | Iteration Time: 1.4061 sec
Training Progress:  13%|█▎        | 26/200 [15:24<1:42:11, 35.24s/it]
[25/200][30/32] 	Loss_D: -0.0963 | Loss_G: -0.4746 | Iteration Time: 1.0222 sec
[26/200][0/32] 	Loss_D: -0.1062 | Loss_G: -0.3967 | Iteration Time: 1.3766 sec
[26/200][5/32] 	Loss_D: -0.1234 | Loss_G: -0.4710 | Iteration Time: 1.3916 sec
[26/200][10/32] 	Loss_D: -0.1149 | Loss_G: -0.4288 | Iteration Time: 1.4182 sec
[26/200][15/32] 	Loss_D: -0.0960 | Loss_G: -0.3864 | Iteration Time: 1.0131 sec
[26/200][20/32] 	Loss_D: -0.1175 | Loss_G: -0.5002 | Iteration Time: 0.9972 sec
[26/200][25/32] 	Loss_D: -0.1164 | Loss_G: -0.4315 | Iteration Time: 1.3892 sec
Training Progress:  14%|█▎        | 27/200 [16:00<1:41:28, 35.19s/it]
[26/200][30/32] 	Loss_D: -0.1085 | Loss_G: -0.4015 | Iteration Time: 1.0206 sec
[27/200][0/32] 	Loss_D: -0.1260 | Loss_G: -0.4259 | Iteration Time: 1.3111 sec
[27/200][5/32] 	Loss_D: -0.1152 | Loss_G: -0.3448 | Iteration Time: 1.3997 sec
[27/200][10/32] 	Loss_D: -0.0800 | Loss_G: -0.2494 | Iteration Time: 1.4091 sec
[27/200][15/32] 	Loss_D: -0.1565 | Loss_G: -0.3480 | Iteration Time: 1.0386 sec
[27/200][20/32] 	Loss_D: -0.0811 | Loss_G: -0.3992 | Iteration Time: 1.0138 sec
[27/200][25/32] 	Loss_D: -0.1399 | Loss_G: -0.4594 | Iteration Time: 1.5092 sec
Training Progress:  14%|█▍        | 28/200 [16:35<1:41:11, 35.30s/it]
[27/200][30/32] 	Loss_D: -0.1229 | Loss_G: -0.4321 | Iteration Time: 1.0436 sec
[28/200][0/32] 	Loss_D: -0.1235 | Loss_G: -0.4287 | Iteration Time: 1.3776 sec
[28/200][5/32] 	Loss_D: -0.1055 | Loss_G: -0.3755 | Iteration Time: 1.3836 sec
[28/200][10/32] 	Loss_D: -0.1048 | Loss_G: -0.2856 | Iteration Time: 1.3919 sec
[28/200][15/32] 	Loss_D: -0.1303 | Loss_G: -0.5023 | Iteration Time: 1.0249 sec
[28/200][20/32] 	Loss_D: -0.1213 | Loss_G: -0.4673 | Iteration Time: 1.0011 sec
[28/200][25/32] 	Loss_D: -0.1460 | Loss_G: -0.4056 | Iteration Time: 1.4028 sec
Training Progress:  14%|█▍        | 29/200 [17:10<1:40:32, 35.28s/it]
[28/200][30/32] 	Loss_D: -0.0211 | Loss_G: -0.6940 | Iteration Time: 1.0131 sec
[29/200][0/32] 	Loss_D: -0.1251 | Loss_G: -0.4349 | Iteration Time: 1.3446 sec
[29/200][5/32] 	Loss_D: -0.1055 | Loss_G: -0.4424 | Iteration Time: 1.3917 sec
[29/200][10/32] 	Loss_D: -0.1474 | Loss_G: -0.4066 | Iteration Time: 1.3792 sec
[29/200][15/32] 	Loss_D: -0.1475 | Loss_G: -0.3044 | Iteration Time: 1.0114 sec
[29/200][20/32] 	Loss_D: -0.1287 | Loss_G: -0.4198 | Iteration Time: 0.9926 sec
[29/200][25/32] 	Loss_D: -0.1260 | Loss_G: -0.3923 | Iteration Time: 1.3809 sec
Training Progress:  15%|█▌        | 30/200 [17:45<1:39:45, 35.21s/it]
[29/200][30/32] 	Loss_D: -0.0744 | Loss_G: -0.3826 | Iteration Time: 1.0331 sec
[30/200][0/32] 	Loss_D: -0.1364 | Loss_G: -0.3989 | Iteration Time: 1.3522 sec
[30/200][5/32] 	Loss_D: -0.1247 | Loss_G: -0.5166 | Iteration Time: 1.4064 sec
[30/200][10/32] 	Loss_D: -0.1529 | Loss_G: -0.3585 | Iteration Time: 1.4016 sec
[30/200][15/32] 	Loss_D: -0.1016 | Loss_G: -0.4048 | Iteration Time: 1.0221 sec
[30/200][20/32] 	Loss_D: -0.1253 | Loss_G: -0.4786 | Iteration Time: 1.0021 sec
[30/200][25/32] 	Loss_D: -0.1443 | Loss_G: -0.4229 | Iteration Time: 1.3921 sec
Training Progress:  16%|█▌        | 31/200 [18:21<1:39:04, 35.17s/it]
[30/200][30/32] 	Loss_D: -0.1341 | Loss_G: -0.4361 | Iteration Time: 1.0376 sec
[31/200][0/32] 	Loss_D: -0.1588 | Loss_G: -0.4581 | Iteration Time: 1.3367 sec
[31/200][5/32] 	Loss_D: -0.0965 | Loss_G: -0.3145 | Iteration Time: 1.3804 sec
Current scores at iteration 1000 | FID: 102.88699340820312 | IS: 2.465172052383423
No description has been provided for this image
[31/200][10/32] 	Loss_D: -0.1230 | Loss_G: -0.4574 | Iteration Time: 1.2986 sec
[31/200][15/32] 	Loss_D: 0.0085 | Loss_G: -0.6737 | Iteration Time: 0.8431 sec
[31/200][20/32] 	Loss_D: -0.1227 | Loss_G: -0.3899 | Iteration Time: 1.3707 sec
[31/200][25/32] 	Loss_D: -0.1482 | Loss_G: -0.4090 | Iteration Time: 0.7441 sec
[31/200][30/32] 	Loss_D: -0.1012 | Loss_G: -0.2844 | Iteration Time: 1.2077 sec
Training Progress:  16%|█▌        | 32/200 [19:01<1:42:49, 36.72s/it]
[32/200][0/32] 	Loss_D: -0.1380 | Loss_G: -0.3945 | Iteration Time: 1.0881 sec
[32/200][5/32] 	Loss_D: -0.1394 | Loss_G: -0.4123 | Iteration Time: 1.3671 sec
[32/200][10/32] 	Loss_D: -0.0324 | Loss_G: -0.3179 | Iteration Time: 0.9291 sec
[32/200][15/32] 	Loss_D: -0.1501 | Loss_G: -0.4130 | Iteration Time: 0.7588 sec
[32/200][20/32] 	Loss_D: -0.1131 | Loss_G: -0.3086 | Iteration Time: 1.2842 sec
[32/200][25/32] 	Loss_D: -0.1451 | Loss_G: -0.4464 | Iteration Time: 0.8121 sec
[32/200][30/32] 	Loss_D: -0.1238 | Loss_G: -0.4457 | Iteration Time: 1.2291 sec
Training Progress:  16%|█▋        | 33/200 [19:35<1:40:00, 35.93s/it]
[33/200][0/32] 	Loss_D: -0.1162 | Loss_G: -0.4053 | Iteration Time: 1.0653 sec
[33/200][5/32] 	Loss_D: -0.1460 | Loss_G: -0.2458 | Iteration Time: 0.9459 sec
[33/200][10/32] 	Loss_D: -0.1256 | Loss_G: -0.3978 | Iteration Time: 0.8236 sec
[33/200][15/32] 	Loss_D: -0.0268 | Loss_G: -0.6757 | Iteration Time: 1.0814 sec
[33/200][20/32] 	Loss_D: -0.1435 | Loss_G: -0.4613 | Iteration Time: 0.9476 sec
[33/200][25/32] 	Loss_D: -0.0848 | Loss_G: -0.4919 | Iteration Time: 1.2676 sec
[33/200][30/32] 	Loss_D: -0.1448 | Loss_G: -0.4389 | Iteration Time: 1.1786 sec
Training Progress:  17%|█▋        | 34/200 [20:10<1:38:19, 35.54s/it]
[34/200][0/32] 	Loss_D: -0.1379 | Loss_G: -0.4014 | Iteration Time: 1.1731 sec
[34/200][5/32] 	Loss_D: -0.1464 | Loss_G: -0.4336 | Iteration Time: 1.3753 sec
[34/200][10/32] 	Loss_D: -0.1511 | Loss_G: -0.3779 | Iteration Time: 0.9826 sec
[34/200][15/32] 	Loss_D: -0.1430 | Loss_G: -0.4547 | Iteration Time: 0.7521 sec
[34/200][20/32] 	Loss_D: -0.1480 | Loss_G: -0.3796 | Iteration Time: 1.2623 sec
[34/200][25/32] 	Loss_D: -0.0978 | Loss_G: -0.3783 | Iteration Time: 0.8106 sec
[34/200][30/32] 	Loss_D: -0.1286 | Loss_G: -0.4222 | Iteration Time: 1.2361 sec
Training Progress:  18%|█▊        | 35/200 [20:44<1:36:35, 35.13s/it]
[35/200][0/32] 	Loss_D: -0.1609 | Loss_G: -0.4190 | Iteration Time: 1.0751 sec
[35/200][5/32] 	Loss_D: -0.1336 | Loss_G: -0.4420 | Iteration Time: 0.9435 sec
[35/200][10/32] 	Loss_D: -0.1020 | Loss_G: -0.3592 | Iteration Time: 0.8261 sec
[35/200][15/32] 	Loss_D: -0.0852 | Loss_G: -0.3847 | Iteration Time: 1.0807 sec
[35/200][20/32] 	Loss_D: -0.1517 | Loss_G: -0.4061 | Iteration Time: 0.9466 sec
[35/200][25/32] 	Loss_D: -0.1613 | Loss_G: -0.3961 | Iteration Time: 1.1919 sec
[35/200][30/32] 	Loss_D: -0.1257 | Loss_G: -0.3542 | Iteration Time: 1.1881 sec
Training Progress:  18%|█▊        | 36/200 [21:18<1:35:26, 34.92s/it]
[36/200][0/32] 	Loss_D: -0.1304 | Loss_G: -0.5063 | Iteration Time: 1.1602 sec
[36/200][5/32] 	Loss_D: -0.1232 | Loss_G: -0.4268 | Iteration Time: 1.3736 sec
[36/200][10/32] 	Loss_D: -0.1135 | Loss_G: -0.4240 | Iteration Time: 1.0091 sec
[36/200][15/32] 	Loss_D: -0.1247 | Loss_G: -0.5044 | Iteration Time: 0.7446 sec
[36/200][20/32] 	Loss_D: -0.1557 | Loss_G: -0.4394 | Iteration Time: 1.2716 sec
[36/200][25/32] 	Loss_D: -0.1237 | Loss_G: -0.3037 | Iteration Time: 0.8029 sec
[36/200][30/32] 	Loss_D: -0.1259 | Loss_G: -0.4034 | Iteration Time: 1.3166 sec
Training Progress:  18%|█▊        | 37/200 [21:53<1:34:27, 34.77s/it]
[37/200][0/32] 	Loss_D: -0.1437 | Loss_G: -0.4121 | Iteration Time: 1.0666 sec
[37/200][5/32] 	Loss_D: -0.1473 | Loss_G: -0.3279 | Iteration Time: 0.9376 sec
[37/200][10/32] 	Loss_D: -0.0838 | Loss_G: -0.1950 | Iteration Time: 0.8581 sec
[37/200][15/32] 	Loss_D: -0.1716 | Loss_G: -0.3707 | Iteration Time: 1.0931 sec
[37/200][20/32] 	Loss_D: -0.1368 | Loss_G: -0.4705 | Iteration Time: 0.9541 sec
[37/200][25/32] 	Loss_D: -0.0980 | Loss_G: -0.2981 | Iteration Time: 1.2072 sec
[37/200][30/32] 	Loss_D: -0.1514 | Loss_G: -0.3881 | Iteration Time: 1.2211 sec
Training Progress:  19%|█▉        | 38/200 [22:27<1:33:49, 34.75s/it]
[38/200][0/32] 	Loss_D: -0.1463 | Loss_G: -0.4081 | Iteration Time: 1.1468 sec
[38/200][5/32] 	Loss_D: -0.0727 | Loss_G: -0.4921 | Iteration Time: 1.3826 sec
[38/200][10/32] 	Loss_D: -0.1532 | Loss_G: -0.3880 | Iteration Time: 1.0176 sec
[38/200][15/32] 	Loss_D: -0.1213 | Loss_G: -0.3993 | Iteration Time: 0.7471 sec
[38/200][20/32] 	Loss_D: -0.1580 | Loss_G: -0.4377 | Iteration Time: 1.2666 sec
[38/200][25/32] 	Loss_D: -0.1238 | Loss_G: -0.4415 | Iteration Time: 0.8016 sec
[38/200][30/32] 	Loss_D: -0.1517 | Loss_G: -0.4110 | Iteration Time: 1.2918 sec
Training Progress:  20%|█▉        | 39/200 [23:02<1:32:56, 34.64s/it]
[39/200][0/32] 	Loss_D: -0.1542 | Loss_G: -0.4112 | Iteration Time: 1.0812 sec
[39/200][5/32] 	Loss_D: -0.1351 | Loss_G: -0.2681 | Iteration Time: 0.9411 sec
[39/200][10/32] 	Loss_D: -0.1772 | Loss_G: -0.3860 | Iteration Time: 0.8236 sec
[39/200][15/32] 	Loss_D: -0.1744 | Loss_G: -0.3921 | Iteration Time: 1.0827 sec
[39/200][20/32] 	Loss_D: -0.1158 | Loss_G: -0.3606 | Iteration Time: 0.9601 sec
[39/200][25/32] 	Loss_D: -0.1112 | Loss_G: -0.5701 | Iteration Time: 1.1936 sec
[39/200][30/32] 	Loss_D: -0.1208 | Loss_G: -0.3973 | Iteration Time: 1.2006 sec
Training Progress:  20%|██        | 40/200 [23:36<1:32:06, 34.54s/it]
[40/200][0/32] 	Loss_D: -0.1608 | Loss_G: -0.3669 | Iteration Time: 1.1687 sec
[40/200][5/32] 	Loss_D: -0.1493 | Loss_G: -0.4258 | Iteration Time: 1.3296 sec
[40/200][10/32] 	Loss_D: -0.1147 | Loss_G: -0.3061 | Iteration Time: 1.0095 sec
[40/200][15/32] 	Loss_D: -0.1318 | Loss_G: -0.2954 | Iteration Time: 0.7596 sec
[40/200][20/32] 	Loss_D: -0.1566 | Loss_G: -0.4210 | Iteration Time: 1.2722 sec
[40/200][25/32] 	Loss_D: -0.1250 | Loss_G: -0.4788 | Iteration Time: 0.7935 sec
[40/200][30/32] 	Loss_D: -0.1608 | Loss_G: -0.3872 | Iteration Time: 1.4182 sec
Training Progress:  20%|██        | 41/200 [24:11<1:31:41, 34.60s/it]
[41/200][0/32] 	Loss_D: -0.1620 | Loss_G: -0.3792 | Iteration Time: 1.1466 sec
[41/200][5/32] 	Loss_D: -0.1470 | Loss_G: -0.4205 | Iteration Time: 0.9791 sec
[41/200][10/32] 	Loss_D: -0.1461 | Loss_G: -0.4444 | Iteration Time: 0.9556 sec
[41/200][15/32] 	Loss_D: 0.0311 | Loss_G: -0.7389 | Iteration Time: 0.9948 sec
[41/200][20/32] 	Loss_D: -0.1551 | Loss_G: -0.3427 | Iteration Time: 0.8367 sec
[41/200][25/32] 	Loss_D: -0.1527 | Loss_G: -0.3908 | Iteration Time: 1.3512 sec
[41/200][30/32] 	Loss_D: -0.1255 | Loss_G: -0.4338 | Iteration Time: 1.2026 sec
Training Progress:  21%|██        | 42/200 [24:46<1:31:24, 34.71s/it]
[42/200][0/32] 	Loss_D: -0.1878 | Loss_G: -0.4479 | Iteration Time: 1.2272 sec
[42/200][5/32] 	Loss_D: -0.1562 | Loss_G: -0.4037 | Iteration Time: 1.4367 sec
[42/200][10/32] 	Loss_D: -0.1507 | Loss_G: -0.4324 | Iteration Time: 0.9538 sec
[42/200][15/32] 	Loss_D: -0.1650 | Loss_G: -0.4404 | Iteration Time: 0.7181 sec
[42/200][20/32] 	Loss_D: -0.1120 | Loss_G: -0.3602 | Iteration Time: 1.1226 sec
[42/200][25/32] 	Loss_D: -0.1527 | Loss_G: -0.4040 | Iteration Time: 0.7091 sec
[42/200][30/32] 	Loss_D: -0.0986 | Loss_G: -0.4513 | Iteration Time: 1.0941 sec
Training Progress:  22%|██▏       | 43/200 [25:18<1:29:14, 34.11s/it]
[43/200][0/32] 	Loss_D: -0.1648 | Loss_G: -0.3911 | Iteration Time: 1.0652 sec
[43/200][5/32] 	Loss_D: -0.1681 | Loss_G: -0.4331 | Iteration Time: 0.9573 sec
[43/200][10/32] 	Loss_D: -0.1502 | Loss_G: -0.4035 | Iteration Time: 0.7807 sec
[43/200][15/32] 	Loss_D: -0.1675 | Loss_G: -0.3165 | Iteration Time: 1.0303 sec
[43/200][20/32] 	Loss_D: -0.1475 | Loss_G: -0.4059 | Iteration Time: 0.8964 sec
[43/200][25/32] 	Loss_D: -0.1772 | Loss_G: -0.4118 | Iteration Time: 1.1311 sec
[43/200][30/32] 	Loss_D: -0.1747 | Loss_G: -0.4039 | Iteration Time: 1.1227 sec
Training Progress:  22%|██▏       | 44/200 [25:51<1:27:45, 33.75s/it]
[44/200][0/32] 	Loss_D: -0.1465 | Loss_G: -0.4177 | Iteration Time: 1.1741 sec
[44/200][5/32] 	Loss_D: -0.1714 | Loss_G: -0.3986 | Iteration Time: 1.3672 sec
[44/200][10/32] 	Loss_D: -0.1692 | Loss_G: -0.4263 | Iteration Time: 0.9536 sec
[44/200][15/32] 	Loss_D: -0.1755 | Loss_G: -0.3875 | Iteration Time: 0.8156 sec
[44/200][20/32] 	Loss_D: -0.1487 | Loss_G: -0.3237 | Iteration Time: 1.3777 sec
[44/200][25/32] 	Loss_D: -0.1581 | Loss_G: -0.2854 | Iteration Time: 0.9201 sec
[44/200][30/32] 	Loss_D: -0.1807 | Loss_G: -0.3785 | Iteration Time: 1.4757 sec
Training Progress:  22%|██▎       | 45/200 [26:28<1:29:37, 34.70s/it]
[45/200][0/32] 	Loss_D: -0.1633 | Loss_G: -0.4151 | Iteration Time: 1.0366 sec
[45/200][5/32] 	Loss_D: -0.1631 | Loss_G: -0.4127 | Iteration Time: 0.8606 sec
[45/200][10/32] 	Loss_D: -0.1147 | Loss_G: -0.2572 | Iteration Time: 0.8421 sec
[45/200][15/32] 	Loss_D: -0.1350 | Loss_G: -0.3029 | Iteration Time: 1.1782 sec
[45/200][20/32] 	Loss_D: -0.1354 | Loss_G: -0.3581 | Iteration Time: 0.9516 sec
[45/200][25/32] 	Loss_D: -0.1471 | Loss_G: -0.2645 | Iteration Time: 1.1946 sec
[45/200][30/32] 	Loss_D: -0.1531 | Loss_G: -0.3601 | Iteration Time: 1.3476 sec
Training Progress:  23%|██▎       | 46/200 [27:03<1:28:55, 34.65s/it]
[46/200][0/32] 	Loss_D: -0.1201 | Loss_G: -0.4594 | Iteration Time: 1.2211 sec
[46/200][5/32] 	Loss_D: -0.1670 | Loss_G: -0.4303 | Iteration Time: 1.3572 sec
[46/200][10/32] 	Loss_D: -0.1808 | Loss_G: -0.4356 | Iteration Time: 1.1336 sec
[46/200][15/32] 	Loss_D: -0.1694 | Loss_G: -0.3496 | Iteration Time: 0.7981 sec
[46/200][20/32] 	Loss_D: -0.1613 | Loss_G: -0.4315 | Iteration Time: 1.3031 sec
[46/200][25/32] 	Loss_D: -0.1715 | Loss_G: -0.4314 | Iteration Time: 0.8276 sec
Current scores at iteration 1500 | FID: 101.88894653320312 | IS: 2.2773938179016113
No description has been provided for this image
Training Progress:  24%|██▎       | 47/200 [27:44<1:33:06, 36.51s/it]
[46/200][30/32] 	Loss_D: -0.1246 | Loss_G: -0.4690 | Iteration Time: 0.8581 sec
[47/200][0/32] 	Loss_D: -0.1578 | Loss_G: -0.4183 | Iteration Time: 1.0631 sec
[47/200][5/32] 	Loss_D: 0.0360 | Loss_G: -0.7379 | Iteration Time: 1.2841 sec
[47/200][10/32] 	Loss_D: -0.1605 | Loss_G: -0.3659 | Iteration Time: 1.0531 sec
[47/200][15/32] 	Loss_D: -0.1610 | Loss_G: -0.3732 | Iteration Time: 1.4093 sec
[47/200][20/32] 	Loss_D: -0.1636 | Loss_G: -0.4710 | Iteration Time: 0.8356 sec
[47/200][25/32] 	Loss_D: -0.1623 | Loss_G: -0.3900 | Iteration Time: 1.0522 sec
[47/200][30/32] 	Loss_D: -0.1488 | Loss_G: -0.2930 | Iteration Time: 0.9641 sec
Training Progress:  24%|██▍       | 48/200 [28:20<1:32:21, 36.46s/it]
[48/200][0/32] 	Loss_D: -0.1701 | Loss_G: -0.4042 | Iteration Time: 1.0387 sec
[48/200][5/32] 	Loss_D: -0.1696 | Loss_G: -0.3928 | Iteration Time: 1.1136 sec
[48/200][10/32] 	Loss_D: -0.1527 | Loss_G: -0.3983 | Iteration Time: 0.9884 sec
[48/200][15/32] 	Loss_D: -0.1465 | Loss_G: -0.4858 | Iteration Time: 1.3806 sec
[48/200][20/32] 	Loss_D: -0.1590 | Loss_G: -0.4402 | Iteration Time: 1.2727 sec
[48/200][25/32] 	Loss_D: -0.1688 | Loss_G: -0.4214 | Iteration Time: 1.2506 sec
Training Progress:  24%|██▍       | 49/200 [28:57<1:32:26, 36.73s/it]
[48/200][30/32] 	Loss_D: -0.1752 | Loss_G: -0.4040 | Iteration Time: 0.9611 sec
[49/200][0/32] 	Loss_D: -0.0829 | Loss_G: -0.3914 | Iteration Time: 1.0266 sec
[49/200][5/32] 	Loss_D: -0.1570 | Loss_G: -0.4462 | Iteration Time: 1.1341 sec
[49/200][10/32] 	Loss_D: -0.1815 | Loss_G: -0.3657 | Iteration Time: 0.9849 sec
[49/200][15/32] 	Loss_D: -0.1365 | Loss_G: -0.3595 | Iteration Time: 1.3836 sec
[49/200][20/32] 	Loss_D: -0.1566 | Loss_G: -0.3898 | Iteration Time: 1.2796 sec
[49/200][25/32] 	Loss_D: -0.0503 | Loss_G: -0.6776 | Iteration Time: 1.2577 sec
Training Progress:  25%|██▌       | 50/200 [29:35<1:32:27, 36.98s/it]
[49/200][30/32] 	Loss_D: -0.0920 | Loss_G: -0.2360 | Iteration Time: 0.9456 sec
[50/200][0/32] 	Loss_D: -0.1676 | Loss_G: -0.4296 | Iteration Time: 1.0241 sec
[50/200][5/32] 	Loss_D: -0.1571 | Loss_G: -0.3895 | Iteration Time: 1.1146 sec
[50/200][10/32] 	Loss_D: -0.1419 | Loss_G: -0.3722 | Iteration Time: 0.9931 sec
[50/200][15/32] 	Loss_D: 0.1408 | Loss_G: -0.7314 | Iteration Time: 1.4516 sec
[50/200][20/32] 	Loss_D: -0.1845 | Loss_G: -0.4167 | Iteration Time: 1.4632 sec
[50/200][25/32] 	Loss_D: -0.1812 | Loss_G: -0.3621 | Iteration Time: 1.3631 sec
Training Progress:  26%|██▌       | 51/200 [30:14<1:33:24, 37.61s/it]
[50/200][30/32] 	Loss_D: -0.1572 | Loss_G: -0.3494 | Iteration Time: 0.9506 sec
[51/200][0/32] 	Loss_D: -0.1212 | Loss_G: -0.3385 | Iteration Time: 1.0151 sec
[51/200][5/32] 	Loss_D: -0.1783 | Loss_G: -0.3929 | Iteration Time: 1.1096 sec
[51/200][10/32] 	Loss_D: -0.1545 | Loss_G: -0.3230 | Iteration Time: 1.0024 sec
[51/200][15/32] 	Loss_D: -0.1329 | Loss_G: -0.5975 | Iteration Time: 1.3764 sec
[51/200][20/32] 	Loss_D: -0.1703 | Loss_G: -0.3863 | Iteration Time: 1.2896 sec
[51/200][25/32] 	Loss_D: -0.1375 | Loss_G: -0.2386 | Iteration Time: 1.2641 sec
Training Progress:  26%|██▌       | 52/200 [30:52<1:32:46, 37.61s/it]
[51/200][30/32] 	Loss_D: -0.1797 | Loss_G: -0.4390 | Iteration Time: 1.0711 sec
[52/200][0/32] 	Loss_D: -0.1172 | Loss_G: -0.5133 | Iteration Time: 1.1506 sec
[52/200][5/32] 	Loss_D: -0.1770 | Loss_G: -0.3508 | Iteration Time: 1.1886 sec
[52/200][10/32] 	Loss_D: -0.1457 | Loss_G: -0.3027 | Iteration Time: 1.0367 sec
[52/200][15/32] 	Loss_D: -0.1759 | Loss_G: -0.4118 | Iteration Time: 1.4467 sec
[52/200][20/32] 	Loss_D: -0.1471 | Loss_G: -0.4448 | Iteration Time: 1.3851 sec
[52/200][25/32] 	Loss_D: -0.1440 | Loss_G: -0.3885 | Iteration Time: 1.3707 sec
Training Progress:  26%|██▋       | 53/200 [31:32<1:34:26, 38.55s/it]
[52/200][30/32] 	Loss_D: -0.1795 | Loss_G: -0.3888 | Iteration Time: 1.0316 sec
[53/200][0/32] 	Loss_D: -0.1818 | Loss_G: -0.4518 | Iteration Time: 1.0717 sec
[53/200][5/32] 	Loss_D: -0.1807 | Loss_G: -0.3515 | Iteration Time: 1.1716 sec
[53/200][10/32] 	Loss_D: -0.1807 | Loss_G: -0.3262 | Iteration Time: 1.0280 sec
[53/200][15/32] 	Loss_D: -0.1592 | Loss_G: -0.4351 | Iteration Time: 1.4677 sec
[53/200][20/32] 	Loss_D: -0.1447 | Loss_G: -0.2925 | Iteration Time: 1.3646 sec
[53/200][25/32] 	Loss_D: -0.1708 | Loss_G: -0.4560 | Iteration Time: 1.3311 sec
Training Progress:  27%|██▋       | 54/200 [32:12<1:34:37, 38.88s/it]
[53/200][30/32] 	Loss_D: -0.1677 | Loss_G: -0.4087 | Iteration Time: 1.0231 sec
[54/200][0/32] 	Loss_D: -0.1763 | Loss_G: -0.4104 | Iteration Time: 1.1147 sec
[54/200][5/32] 	Loss_D: -0.1687 | Loss_G: -0.4140 | Iteration Time: 1.1957 sec
[54/200][10/32] 	Loss_D: -0.1671 | Loss_G: -0.4211 | Iteration Time: 1.0576 sec
[54/200][15/32] 	Loss_D: -0.1648 | Loss_G: -0.3257 | Iteration Time: 1.5452 sec
[54/200][20/32] 	Loss_D: -0.1562 | Loss_G: -0.3872 | Iteration Time: 1.5127 sec
[54/200][25/32] 	Loss_D: -0.1794 | Loss_G: -0.4712 | Iteration Time: 1.4387 sec
Training Progress:  28%|██▊       | 55/200 [32:52<1:35:08, 39.37s/it]
[54/200][30/32] 	Loss_D: -0.1290 | Loss_G: -0.4120 | Iteration Time: 0.9301 sec
[55/200][0/32] 	Loss_D: -0.1755 | Loss_G: -0.4045 | Iteration Time: 0.9956 sec
[55/200][5/32] 	Loss_D: -0.1588 | Loss_G: -0.3469 | Iteration Time: 1.1006 sec
[55/200][10/32] 	Loss_D: -0.0700 | Loss_G: -0.2431 | Iteration Time: 0.9806 sec
[55/200][15/32] 	Loss_D: -0.1630 | Loss_G: -0.3435 | Iteration Time: 1.3742 sec
[55/200][20/32] 	Loss_D: -0.1473 | Loss_G: -0.3206 | Iteration Time: 1.2512 sec
[55/200][25/32] 	Loss_D: -0.1435 | Loss_G: -0.2413 | Iteration Time: 1.2262 sec
Training Progress:  28%|██▊       | 56/200 [33:30<1:32:53, 38.70s/it]
[55/200][30/32] 	Loss_D: -0.1690 | Loss_G: -0.4449 | Iteration Time: 0.9176 sec
[56/200][0/32] 	Loss_D: -0.1825 | Loss_G: -0.3759 | Iteration Time: 0.9831 sec
[56/200][5/32] 	Loss_D: -0.1772 | Loss_G: -0.3430 | Iteration Time: 1.1236 sec
[56/200][10/32] 	Loss_D: -0.1831 | Loss_G: -0.4037 | Iteration Time: 1.0761 sec
[56/200][15/32] 	Loss_D: -0.1815 | Loss_G: -0.3968 | Iteration Time: 1.5192 sec
[56/200][20/32] 	Loss_D: -0.1787 | Loss_G: -0.4098 | Iteration Time: 1.4312 sec
[56/200][25/32] 	Loss_D: -0.1533 | Loss_G: -0.4514 | Iteration Time: 1.4188 sec
Training Progress:  28%|██▊       | 57/200 [34:10<1:33:42, 39.32s/it]
[56/200][30/32] 	Loss_D: -0.1695 | Loss_G: -0.3675 | Iteration Time: 1.0727 sec
[57/200][0/32] 	Loss_D: -0.1677 | Loss_G: -0.4254 | Iteration Time: 1.1582 sec
[57/200][5/32] 	Loss_D: -0.1573 | Loss_G: -0.3829 | Iteration Time: 1.2092 sec
[57/200][10/32] 	Loss_D: -0.1010 | Loss_G: -0.3290 | Iteration Time: 1.0716 sec
[57/200][15/32] 	Loss_D: -0.0736 | Loss_G: -0.5659 | Iteration Time: 1.4957 sec
[57/200][20/32] 	Loss_D: -0.1782 | Loss_G: -0.4112 | Iteration Time: 1.3771 sec
[57/200][25/32] 	Loss_D: -0.1881 | Loss_G: -0.3746 | Iteration Time: 1.5372 sec
Training Progress:  29%|██▉       | 58/200 [34:51<1:33:59, 39.71s/it]
[57/200][30/32] 	Loss_D: -0.1833 | Loss_G: -0.3893 | Iteration Time: 1.0321 sec
[58/200][0/32] 	Loss_D: -0.1662 | Loss_G: -0.4019 | Iteration Time: 0.9656 sec
[58/200][5/32] 	Loss_D: -0.1398 | Loss_G: -0.2843 | Iteration Time: 1.0817 sec
[58/200][10/32] 	Loss_D: -0.1890 | Loss_G: -0.4009 | Iteration Time: 1.0031 sec
[58/200][15/32] 	Loss_D: -0.1745 | Loss_G: -0.3849 | Iteration Time: 1.4071 sec
[58/200][20/32] 	Loss_D: -0.1795 | Loss_G: -0.4382 | Iteration Time: 1.2776 sec
[58/200][25/32] 	Loss_D: -0.1435 | Loss_G: -0.4103 | Iteration Time: 1.2166 sec
Training Progress:  30%|██▉       | 59/200 [35:28<1:31:27, 38.92s/it]
[58/200][30/32] 	Loss_D: -0.1757 | Loss_G: -0.3928 | Iteration Time: 0.9266 sec
[59/200][0/32] 	Loss_D: -0.1457 | Loss_G: -0.4367 | Iteration Time: 0.9897 sec
[59/200][5/32] 	Loss_D: -0.2041 | Loss_G: -0.3299 | Iteration Time: 1.0766 sec
[59/200][10/32] 	Loss_D: -0.1733 | Loss_G: -0.2679 | Iteration Time: 0.9690 sec
[59/200][15/32] 	Loss_D: -0.1816 | Loss_G: -0.3922 | Iteration Time: 1.3621 sec
[59/200][20/32] 	Loss_D: -0.1795 | Loss_G: -0.3498 | Iteration Time: 1.3176 sec
[59/200][25/32] 	Loss_D: -0.1521 | Loss_G: -0.4042 | Iteration Time: 1.2286 sec
Training Progress:  30%|███       | 60/200 [36:05<1:29:21, 38.30s/it]
[59/200][30/32] 	Loss_D: -0.1635 | Loss_G: -0.3944 | Iteration Time: 0.9801 sec
[60/200][0/32] 	Loss_D: -0.1999 | Loss_G: -0.3708 | Iteration Time: 1.0706 sec
[60/200][5/32] 	Loss_D: -0.1695 | Loss_G: -0.3595 | Iteration Time: 1.0956 sec
[60/200][10/32] 	Loss_D: -0.1931 | Loss_G: -0.4460 | Iteration Time: 0.9861 sec
[60/200][15/32] 	Loss_D: -0.1748 | Loss_G: -0.3790 | Iteration Time: 1.5237 sec
[60/200][20/32] 	Loss_D: -0.1732 | Loss_G: -0.3363 | Iteration Time: 1.2617 sec
[60/200][25/32] 	Loss_D: -0.1806 | Loss_G: -0.4186 | Iteration Time: 1.3181 sec
Training Progress:  30%|███       | 61/200 [36:43<1:28:27, 38.18s/it]
[60/200][30/32] 	Loss_D: -0.1124 | Loss_G: -0.1934 | Iteration Time: 0.9041 sec
[61/200][0/32] 	Loss_D: -0.1510 | Loss_G: -0.4881 | Iteration Time: 0.9631 sec
[61/200][5/32] 	Loss_D: -0.1849 | Loss_G: -0.4170 | Iteration Time: 1.0947 sec
[61/200][10/32] 	Loss_D: -0.1587 | Loss_G: -0.4709 | Iteration Time: 1.0282 sec
[61/200][15/32] 	Loss_D: -0.1830 | Loss_G: -0.4517 | Iteration Time: 1.5977 sec
[61/200][20/32] 	Loss_D: -0.1656 | Loss_G: -0.4270 | Iteration Time: 1.4266 sec
[61/200][25/32] 	Loss_D: -0.1572 | Loss_G: -0.6869 | Iteration Time: 1.3866 sec
Training Progress:  31%|███       | 62/200 [37:23<1:29:02, 38.72s/it]
[61/200][30/32] 	Loss_D: -0.1724 | Loss_G: -0.3735 | Iteration Time: 1.0761 sec
[62/200][0/32] 	Loss_D: -0.1883 | Loss_G: -0.3915 | Iteration Time: 1.1672 sec
[62/200][5/32] 	Loss_D: -0.1875 | Loss_G: -0.3828 | Iteration Time: 1.2106 sec
[62/200][10/32] 	Loss_D: -0.1847 | Loss_G: -0.3413 | Iteration Time: 1.1146 sec
[62/200][15/32] 	Loss_D: -0.1520 | Loss_G: -0.4441 | Iteration Time: 1.3311 sec
Current scores at iteration 2000 | FID: 86.91372680664062 | IS: 2.449937105178833
No description has been provided for this image
[62/200][20/32] 	Loss_D: -0.1846 | Loss_G: -0.3697 | Iteration Time: 0.9486 sec
[62/200][25/32] 	Loss_D: -0.1970 | Loss_G: -0.3766 | Iteration Time: 1.1121 sec
[62/200][30/32] 	Loss_D: 0.0903 | Loss_G: -0.2575 | Iteration Time: 1.1942 sec
Training Progress:  32%|███▏      | 63/200 [38:08<1:32:46, 40.63s/it]
[63/200][0/32] 	Loss_D: -0.1769 | Loss_G: -0.3962 | Iteration Time: 1.0706 sec
[63/200][5/32] 	Loss_D: -0.1620 | Loss_G: -0.3861 | Iteration Time: 1.0851 sec
[63/200][10/32] 	Loss_D: -0.1606 | Loss_G: -0.4461 | Iteration Time: 0.8636 sec
[63/200][15/32] 	Loss_D: -0.2054 | Loss_G: -0.3216 | Iteration Time: 1.2821 sec
[63/200][20/32] 	Loss_D: -0.1474 | Loss_G: -0.5177 | Iteration Time: 0.7501 sec
[63/200][25/32] 	Loss_D: -0.1388 | Loss_G: -0.5270 | Iteration Time: 1.0536 sec
[63/200][30/32] 	Loss_D: -0.1724 | Loss_G: -0.3504 | Iteration Time: 1.1541 sec
Training Progress:  32%|███▏      | 64/200 [38:41<1:27:05, 38.42s/it]
[64/200][0/32] 	Loss_D: -0.1860 | Loss_G: -0.4655 | Iteration Time: 1.0401 sec
[64/200][5/32] 	Loss_D: -0.1759 | Loss_G: -0.3626 | Iteration Time: 1.1352 sec
[64/200][10/32] 	Loss_D: -0.1450 | Loss_G: -0.3788 | Iteration Time: 0.7901 sec
[64/200][15/32] 	Loss_D: -0.1793 | Loss_G: -0.4751 | Iteration Time: 1.2356 sec
[64/200][20/32] 	Loss_D: -0.1439 | Loss_G: -0.2148 | Iteration Time: 0.7346 sec
[64/200][25/32] 	Loss_D: -0.1949 | Loss_G: -0.3421 | Iteration Time: 1.0511 sec
[64/200][30/32] 	Loss_D: -0.1960 | Loss_G: -0.4061 | Iteration Time: 1.1231 sec
Training Progress:  32%|███▎      | 65/200 [39:13<1:22:04, 36.48s/it]
[65/200][0/32] 	Loss_D: -0.1414 | Loss_G: -0.2690 | Iteration Time: 1.0351 sec
[65/200][5/32] 	Loss_D: -0.1908 | Loss_G: -0.3791 | Iteration Time: 1.0966 sec
[65/200][10/32] 	Loss_D: -0.2006 | Loss_G: -0.3351 | Iteration Time: 0.8211 sec
[65/200][15/32] 	Loss_D: -0.1899 | Loss_G: -0.3891 | Iteration Time: 1.3251 sec
[65/200][20/32] 	Loss_D: -0.1587 | Loss_G: -0.4711 | Iteration Time: 0.7656 sec
[65/200][25/32] 	Loss_D: -0.1893 | Loss_G: -0.4182 | Iteration Time: 1.0731 sec
[65/200][30/32] 	Loss_D: -0.1695 | Loss_G: -0.3925 | Iteration Time: 1.2272 sec
Training Progress:  33%|███▎      | 66/200 [39:46<1:19:02, 35.39s/it]
[66/200][0/32] 	Loss_D: -0.1745 | Loss_G: -0.4718 | Iteration Time: 1.0686 sec
[66/200][5/32] 	Loss_D: -0.2125 | Loss_G: -0.3671 | Iteration Time: 1.1531 sec
[66/200][10/32] 	Loss_D: -0.2040 | Loss_G: -0.3532 | Iteration Time: 0.7761 sec
[66/200][15/32] 	Loss_D: -0.1858 | Loss_G: -0.3474 | Iteration Time: 1.2946 sec
[66/200][20/32] 	Loss_D: -0.1816 | Loss_G: -0.4722 | Iteration Time: 0.8221 sec
[66/200][25/32] 	Loss_D: -0.1739 | Loss_G: -0.3894 | Iteration Time: 1.0651 sec
[66/200][30/32] 	Loss_D: -0.1874 | Loss_G: -0.3689 | Iteration Time: 1.1521 sec
Training Progress:  34%|███▎      | 67/200 [40:19<1:16:59, 34.74s/it]
[67/200][0/32] 	Loss_D: -0.1984 | Loss_G: -0.3729 | Iteration Time: 1.0571 sec
[67/200][5/32] 	Loss_D: -0.1833 | Loss_G: -0.3599 | Iteration Time: 1.0891 sec
[67/200][10/32] 	Loss_D: -0.1917 | Loss_G: -0.3893 | Iteration Time: 0.7571 sec
[67/200][15/32] 	Loss_D: -0.2039 | Loss_G: -0.4706 | Iteration Time: 1.3101 sec
[67/200][20/32] 	Loss_D: -0.1719 | Loss_G: -0.4008 | Iteration Time: 0.8346 sec
[67/200][25/32] 	Loss_D: -0.1103 | Loss_G: -0.4678 | Iteration Time: 1.0901 sec
[67/200][30/32] 	Loss_D: -0.1779 | Loss_G: -0.4692 | Iteration Time: 1.1636 sec
Training Progress:  34%|███▍      | 68/200 [40:52<1:14:59, 34.08s/it]
[68/200][0/32] 	Loss_D: -0.1642 | Loss_G: -0.4440 | Iteration Time: 1.0976 sec
[68/200][5/32] 	Loss_D: -0.2000 | Loss_G: -0.3790 | Iteration Time: 1.1071 sec
[68/200][10/32] 	Loss_D: -0.2027 | Loss_G: -0.3316 | Iteration Time: 0.7706 sec
[68/200][15/32] 	Loss_D: -0.1776 | Loss_G: -0.2843 | Iteration Time: 1.3672 sec
[68/200][20/32] 	Loss_D: -0.2064 | Loss_G: -0.3642 | Iteration Time: 0.7526 sec
[68/200][25/32] 	Loss_D: -0.1655 | Loss_G: -0.6098 | Iteration Time: 1.0701 sec
[68/200][30/32] 	Loss_D: -0.1951 | Loss_G: -0.3846 | Iteration Time: 1.1521 sec
Training Progress:  34%|███▍      | 69/200 [41:24<1:13:33, 33.69s/it]
[69/200][0/32] 	Loss_D: -0.1984 | Loss_G: -0.4009 | Iteration Time: 1.0846 sec
[69/200][5/32] 	Loss_D: -0.1813 | Loss_G: -0.3896 | Iteration Time: 1.0726 sec
[69/200][10/32] 	Loss_D: -0.1695 | Loss_G: -0.2601 | Iteration Time: 0.7626 sec
[69/200][15/32] 	Loss_D: -0.1397 | Loss_G: -0.2276 | Iteration Time: 1.2512 sec
[69/200][20/32] 	Loss_D: -0.1875 | Loss_G: -0.3597 | Iteration Time: 0.7351 sec
[69/200][25/32] 	Loss_D: -0.1768 | Loss_G: -0.4939 | Iteration Time: 1.0762 sec
[69/200][30/32] 	Loss_D: -0.1289 | Loss_G: -0.2100 | Iteration Time: 1.1346 sec
Training Progress:  35%|███▌      | 70/200 [41:57<1:12:05, 33.27s/it]
[70/200][0/32] 	Loss_D: -0.1987 | Loss_G: -0.4012 | Iteration Time: 1.0996 sec
[70/200][5/32] 	Loss_D: -0.1882 | Loss_G: -0.3948 | Iteration Time: 1.0801 sec
[70/200][10/32] 	Loss_D: -0.1795 | Loss_G: -0.4457 | Iteration Time: 0.8361 sec
[70/200][15/32] 	Loss_D: -0.2210 | Loss_G: -0.4042 | Iteration Time: 1.2897 sec
[70/200][20/32] 	Loss_D: -0.1952 | Loss_G: -0.4133 | Iteration Time: 0.8241 sec
[70/200][25/32] 	Loss_D: -0.2020 | Loss_G: -0.3229 | Iteration Time: 1.1661 sec
[70/200][30/32] 	Loss_D: -0.1825 | Loss_G: -0.3841 | Iteration Time: 1.2641 sec
Training Progress:  36%|███▌      | 71/200 [42:31<1:11:53, 33.44s/it]
[71/200][0/32] 	Loss_D: -0.0968 | Loss_G: -0.3628 | Iteration Time: 1.1896 sec
[71/200][5/32] 	Loss_D: -0.2202 | Loss_G: -0.4265 | Iteration Time: 1.0911 sec
[71/200][10/32] 	Loss_D: -0.1694 | Loss_G: -0.4033 | Iteration Time: 0.7867 sec
[71/200][15/32] 	Loss_D: -0.1610 | Loss_G: -0.4161 | Iteration Time: 1.2876 sec
[71/200][20/32] 	Loss_D: -0.1846 | Loss_G: -0.3678 | Iteration Time: 0.7286 sec
[71/200][25/32] 	Loss_D: -0.1906 | Loss_G: -0.4038 | Iteration Time: 1.0461 sec
[71/200][30/32] 	Loss_D: -0.1872 | Loss_G: -0.3982 | Iteration Time: 1.1812 sec
Training Progress:  36%|███▌      | 72/200 [43:03<1:10:50, 33.21s/it]
[72/200][0/32] 	Loss_D: -0.1715 | Loss_G: -0.4186 | Iteration Time: 1.1316 sec
[72/200][5/32] 	Loss_D: -0.2111 | Loss_G: -0.5622 | Iteration Time: 1.1976 sec
[72/200][10/32] 	Loss_D: -0.1807 | Loss_G: -0.4446 | Iteration Time: 0.8061 sec
[72/200][15/32] 	Loss_D: -0.1998 | Loss_G: -0.3541 | Iteration Time: 1.2541 sec
[72/200][20/32] 	Loss_D: -0.1481 | Loss_G: -0.4237 | Iteration Time: 0.8206 sec
[72/200][25/32] 	Loss_D: -0.0673 | Loss_G: -0.2123 | Iteration Time: 1.2051 sec
[72/200][30/32] 	Loss_D: -0.1873 | Loss_G: -0.3956 | Iteration Time: 1.2816 sec
Training Progress:  36%|███▋      | 73/200 [43:38<1:11:05, 33.59s/it]
[73/200][0/32] 	Loss_D: -0.2011 | Loss_G: -0.3853 | Iteration Time: 1.1536 sec
[73/200][5/32] 	Loss_D: -0.2038 | Loss_G: -0.3642 | Iteration Time: 1.2141 sec
[73/200][10/32] 	Loss_D: -0.1526 | Loss_G: -0.3698 | Iteration Time: 0.8466 sec
[73/200][15/32] 	Loss_D: -0.1619 | Loss_G: -0.7025 | Iteration Time: 1.3827 sec
[73/200][20/32] 	Loss_D: -0.1904 | Loss_G: -0.3914 | Iteration Time: 0.8426 sec
[73/200][25/32] 	Loss_D: -0.1784 | Loss_G: -0.5530 | Iteration Time: 1.2041 sec
[73/200][30/32] 	Loss_D: -0.1964 | Loss_G: -0.3951 | Iteration Time: 1.2081 sec
Training Progress:  37%|███▋      | 74/200 [44:13<1:11:29, 34.04s/it]
[74/200][0/32] 	Loss_D: -0.1879 | Loss_G: -0.3867 | Iteration Time: 1.2216 sec
[74/200][5/32] 	Loss_D: -0.1569 | Loss_G: -0.4363 | Iteration Time: 1.2186 sec
[74/200][10/32] 	Loss_D: -0.1820 | Loss_G: -0.4054 | Iteration Time: 0.8436 sec
[74/200][15/32] 	Loss_D: -0.2054 | Loss_G: -0.3287 | Iteration Time: 1.3751 sec
[74/200][20/32] 	Loss_D: -0.1858 | Loss_G: -0.2900 | Iteration Time: 0.7281 sec
[74/200][25/32] 	Loss_D: -0.2070 | Loss_G: -0.2704 | Iteration Time: 1.0836 sec
[74/200][30/32] 	Loss_D: -0.1641 | Loss_G: -0.3644 | Iteration Time: 1.2222 sec
Training Progress:  38%|███▊      | 75/200 [44:47<1:10:51, 34.01s/it]
[75/200][0/32] 	Loss_D: -0.2142 | Loss_G: -0.3980 | Iteration Time: 1.1641 sec
[75/200][5/32] 	Loss_D: -0.1912 | Loss_G: -0.4058 | Iteration Time: 1.2232 sec
[75/200][10/32] 	Loss_D: -0.1554 | Loss_G: -0.2129 | Iteration Time: 0.7641 sec
[75/200][15/32] 	Loss_D: -0.2123 | Loss_G: -0.3146 | Iteration Time: 1.2576 sec
[75/200][20/32] 	Loss_D: -0.1727 | Loss_G: -0.4265 | Iteration Time: 0.7696 sec
[75/200][25/32] 	Loss_D: -0.1338 | Loss_G: -0.1450 | Iteration Time: 1.0586 sec
[75/200][30/32] 	Loss_D: -0.1769 | Loss_G: -0.4577 | Iteration Time: 1.1623 sec
Training Progress:  38%|███▊      | 76/200 [45:20<1:09:31, 33.64s/it]
[76/200][0/32] 	Loss_D: -0.2001 | Loss_G: -0.3375 | Iteration Time: 1.0901 sec
[76/200][5/32] 	Loss_D: -0.1785 | Loss_G: -0.3573 | Iteration Time: 1.0671 sec
[76/200][10/32] 	Loss_D: -0.0660 | Loss_G: -0.7262 | Iteration Time: 0.7656 sec
[76/200][15/32] 	Loss_D: -0.2200 | Loss_G: -0.3901 | Iteration Time: 1.2682 sec
[76/200][20/32] 	Loss_D: -0.1726 | Loss_G: -0.2343 | Iteration Time: 0.7576 sec
[76/200][25/32] 	Loss_D: -0.2183 | Loss_G: -0.3115 | Iteration Time: 1.0636 sec
[76/200][30/32] 	Loss_D: -0.2165 | Loss_G: -0.3668 | Iteration Time: 1.1941 sec
Training Progress:  38%|███▊      | 77/200 [45:52<1:08:03, 33.20s/it]
[77/200][0/32] 	Loss_D: -0.1946 | Loss_G: -0.3882 | Iteration Time: 1.0481 sec
[77/200][5/32] 	Loss_D: -0.1942 | Loss_G: -0.2672 | Iteration Time: 1.1136 sec
[77/200][10/32] 	Loss_D: -0.1945 | Loss_G: -0.3413 | Iteration Time: 0.7721 sec
[77/200][15/32] 	Loss_D: -0.1890 | Loss_G: -0.4162 | Iteration Time: 1.2277 sec
[77/200][20/32] 	Loss_D: -0.2080 | Loss_G: -0.3443 | Iteration Time: 0.7551 sec
[77/200][25/32] 	Loss_D: -0.1726 | Loss_G: -0.3831 | Iteration Time: 1.0741 sec
[77/200][30/32] 	Loss_D: -0.1810 | Loss_G: -0.2557 | Iteration Time: 1.2126 sec
Training Progress:  39%|███▉      | 78/200 [46:24<1:06:51, 32.88s/it]
[78/200][0/32] 	Loss_D: -0.1708 | Loss_G: -0.4628 | Iteration Time: 1.1031 sec
Current scores at iteration 2500 | FID: 91.93374633789062 | IS: 2.5088324546813965
No description has been provided for this image
[78/200][5/32] 	Loss_D: -0.1905 | Loss_G: -0.5468 | Iteration Time: 1.4357 sec
[78/200][10/32] 	Loss_D: -0.1494 | Loss_G: -0.6368 | Iteration Time: 1.0652 sec
[78/200][15/32] 	Loss_D: -0.0711 | Loss_G: -0.5713 | Iteration Time: 1.0011 sec
[78/200][20/32] 	Loss_D: -0.1644 | Loss_G: -0.2336 | Iteration Time: 0.6436 sec
[78/200][25/32] 	Loss_D: -0.2138 | Loss_G: -0.3589 | Iteration Time: 1.1366 sec
Training Progress:  40%|███▉      | 79/200 [46:57<1:06:36, 33.03s/it]
[78/200][30/32] 	Loss_D: -0.1929 | Loss_G: -0.4205 | Iteration Time: 0.5365 sec
[79/200][0/32] 	Loss_D: -0.2020 | Loss_G: -0.3909 | Iteration Time: 1.0676 sec
[79/200][5/32] 	Loss_D: -0.2069 | Loss_G: -0.3951 | Iteration Time: 1.0931 sec
[79/200][10/32] 	Loss_D: -0.1544 | Loss_G: -0.3360 | Iteration Time: 0.4195 sec
[79/200][15/32] 	Loss_D: -0.2190 | Loss_G: -0.3626 | Iteration Time: 1.1972 sec
[79/200][20/32] 	Loss_D: -0.2086 | Loss_G: -0.3561 | Iteration Time: 0.8051 sec
[79/200][25/32] 	Loss_D: -0.1656 | Loss_G: -0.3817 | Iteration Time: 1.0732 sec
[79/200][30/32] 	Loss_D: -0.2083 | Loss_G: -0.3952 | Iteration Time: 1.0351 sec
Training Progress:  40%|████      | 80/200 [47:26<1:03:16, 31.64s/it]
[80/200][0/32] 	Loss_D: -0.1956 | Loss_G: -0.3891 | Iteration Time: 0.9701 sec
[80/200][5/32] 	Loss_D: -0.1828 | Loss_G: -0.5316 | Iteration Time: 0.9236 sec
[80/200][10/32] 	Loss_D: -0.2010 | Loss_G: -0.4129 | Iteration Time: 0.5371 sec
[80/200][15/32] 	Loss_D: -0.0867 | Loss_G: -0.1995 | Iteration Time: 0.4170 sec
[80/200][20/32] 	Loss_D: -0.1977 | Loss_G: -0.4395 | Iteration Time: 0.9096 sec
[80/200][25/32] 	Loss_D: -0.2163 | Loss_G: -0.3545 | Iteration Time: 0.5766 sec
Training Progress:  40%|████      | 81/200 [47:52<59:21, 29.93s/it]  
[80/200][30/32] 	Loss_D: -0.1955 | Loss_G: -0.2933 | Iteration Time: 0.9121 sec
[81/200][0/32] 	Loss_D: -0.2158 | Loss_G: -0.3819 | Iteration Time: 1.0447 sec
[81/200][5/32] 	Loss_D: -0.2061 | Loss_G: -0.3718 | Iteration Time: 0.8982 sec
[81/200][10/32] 	Loss_D: -0.2106 | Loss_G: -0.2813 | Iteration Time: 0.8521 sec
[81/200][15/32] 	Loss_D: -0.1610 | Loss_G: -0.1583 | Iteration Time: 0.8167 sec
[81/200][20/32] 	Loss_D: -0.2121 | Loss_G: -0.3679 | Iteration Time: 0.4020 sec
[81/200][25/32] 	Loss_D: -0.1966 | Loss_G: -0.3633 | Iteration Time: 0.9116 sec
Training Progress:  41%|████      | 82/200 [48:20<57:48, 29.40s/it]
[81/200][30/32] 	Loss_D: -0.1705 | Loss_G: -0.3253 | Iteration Time: 0.8161 sec
[82/200][0/32] 	Loss_D: -0.1796 | Loss_G: -0.2895 | Iteration Time: 1.0296 sec
[82/200][5/32] 	Loss_D: 0.1625 | Loss_G: -0.6011 | Iteration Time: 1.0756 sec
[82/200][10/32] 	Loss_D: -0.2371 | Loss_G: -0.3707 | Iteration Time: 0.4296 sec
[82/200][15/32] 	Loss_D: -0.2037 | Loss_G: -0.3566 | Iteration Time: 1.2292 sec
[82/200][20/32] 	Loss_D: -0.1996 | Loss_G: -0.3924 | Iteration Time: 0.8166 sec
[82/200][25/32] 	Loss_D: -0.1578 | Loss_G: -0.4543 | Iteration Time: 1.0671 sec
[82/200][30/32] 	Loss_D: -0.2124 | Loss_G: -0.3928 | Iteration Time: 1.0416 sec
Training Progress:  42%|████▏     | 83/200 [48:48<56:42, 29.08s/it]
[83/200][0/32] 	Loss_D: -0.2114 | Loss_G: -0.4384 | Iteration Time: 0.9742 sec
[83/200][5/32] 	Loss_D: -0.2150 | Loss_G: -0.3524 | Iteration Time: 0.9011 sec
[83/200][10/32] 	Loss_D: -0.1924 | Loss_G: -0.2141 | Iteration Time: 0.5401 sec
[83/200][15/32] 	Loss_D: -0.2140 | Loss_G: -0.4753 | Iteration Time: 0.4145 sec
[83/200][20/32] 	Loss_D: -0.1781 | Loss_G: -0.3215 | Iteration Time: 0.9021 sec
[83/200][25/32] 	Loss_D: -0.1836 | Loss_G: -0.3968 | Iteration Time: 0.6086 sec
Training Progress:  42%|████▏     | 84/200 [49:14<54:25, 28.15s/it]
[83/200][30/32] 	Loss_D: -0.1870 | Loss_G: -0.3757 | Iteration Time: 0.8761 sec
[84/200][0/32] 	Loss_D: -0.1743 | Loss_G: -0.4338 | Iteration Time: 0.9176 sec
[84/200][5/32] 	Loss_D: -0.2268 | Loss_G: -0.4860 | Iteration Time: 0.9771 sec
[84/200][10/32] 	Loss_D: -0.1795 | Loss_G: -0.5050 | Iteration Time: 0.8636 sec
[84/200][15/32] 	Loss_D: -0.2057 | Loss_G: -0.4710 | Iteration Time: 0.8801 sec
[84/200][20/32] 	Loss_D: -0.2116 | Loss_G: -0.4198 | Iteration Time: 0.4786 sec
[84/200][25/32] 	Loss_D: -0.1289 | Loss_G: -0.2645 | Iteration Time: 1.0452 sec
Training Progress:  42%|████▎     | 85/200 [49:45<55:28, 28.95s/it]
[84/200][30/32] 	Loss_D: -0.2035 | Loss_G: -0.3826 | Iteration Time: 0.9666 sec
[85/200][0/32] 	Loss_D: -0.2093 | Loss_G: -0.4160 | Iteration Time: 1.1356 sec
[85/200][5/32] 	Loss_D: -0.2098 | Loss_G: -0.3777 | Iteration Time: 1.1996 sec
[85/200][10/32] 	Loss_D: -0.2345 | Loss_G: -0.3284 | Iteration Time: 0.4690 sec
[85/200][15/32] 	Loss_D: -0.2006 | Loss_G: -0.3230 | Iteration Time: 1.3241 sec
[85/200][20/32] 	Loss_D: -0.2142 | Loss_G: -0.4375 | Iteration Time: 0.9236 sec
[85/200][25/32] 	Loss_D: -0.2233 | Loss_G: -0.2645 | Iteration Time: 1.2171 sec
[85/200][30/32] 	Loss_D: -0.1930 | Loss_G: -0.6012 | Iteration Time: 1.1846 sec
Training Progress:  43%|████▎     | 86/200 [50:17<56:34, 29.78s/it]
[86/200][0/32] 	Loss_D: -0.2288 | Loss_G: -0.4493 | Iteration Time: 1.1241 sec
[86/200][5/32] 	Loss_D: -0.1954 | Loss_G: -0.3970 | Iteration Time: 1.0661 sec
[86/200][10/32] 	Loss_D: -0.2103 | Loss_G: -0.3952 | Iteration Time: 0.5971 sec
[86/200][15/32] 	Loss_D: -0.1789 | Loss_G: -0.3797 | Iteration Time: 0.5146 sec
[86/200][20/32] 	Loss_D: -0.2374 | Loss_G: -0.2878 | Iteration Time: 1.0551 sec
[86/200][25/32] 	Loss_D: -0.2208 | Loss_G: -0.3587 | Iteration Time: 0.6356 sec
Training Progress:  44%|████▎     | 87/200 [50:46<56:02, 29.76s/it]
[86/200][30/32] 	Loss_D: -0.1712 | Loss_G: -0.1819 | Iteration Time: 1.0091 sec
[87/200][0/32] 	Loss_D: -0.1973 | Loss_G: -0.3949 | Iteration Time: 1.0286 sec
[87/200][5/32] 	Loss_D: -0.2014 | Loss_G: -0.4213 | Iteration Time: 1.0261 sec
[87/200][10/32] 	Loss_D: -0.2176 | Loss_G: -0.3359 | Iteration Time: 0.9726 sec
[87/200][15/32] 	Loss_D: -0.1770 | Loss_G: -0.1788 | Iteration Time: 0.9506 sec
[87/200][20/32] 	Loss_D: -0.1950 | Loss_G: -0.3272 | Iteration Time: 0.5031 sec
[87/200][25/32] 	Loss_D: -0.1464 | Loss_G: -0.4644 | Iteration Time: 1.0701 sec
Training Progress:  44%|████▍     | 88/200 [51:19<57:04, 30.58s/it]
[87/200][30/32] 	Loss_D: -0.1974 | Loss_G: -0.3294 | Iteration Time: 0.9971 sec
[88/200][0/32] 	Loss_D: -0.2133 | Loss_G: -0.2965 | Iteration Time: 1.1137 sec
[88/200][5/32] 	Loss_D: -0.2302 | Loss_G: -0.3021 | Iteration Time: 1.2077 sec
[88/200][10/32] 	Loss_D: -0.1982 | Loss_G: -0.2705 | Iteration Time: 0.4816 sec
[88/200][15/32] 	Loss_D: -0.1360 | Loss_G: -0.3847 | Iteration Time: 1.2727 sec
[88/200][20/32] 	Loss_D: -0.2397 | Loss_G: -0.4201 | Iteration Time: 0.8956 sec
[88/200][25/32] 	Loss_D: -0.2351 | Loss_G: -0.3576 | Iteration Time: 1.1421 sec
[88/200][30/32] 	Loss_D: -0.2111 | Loss_G: -0.3397 | Iteration Time: 1.1156 sec
Training Progress:  44%|████▍     | 89/200 [51:50<56:41, 30.64s/it]
[89/200][0/32] 	Loss_D: -0.2070 | Loss_G: -0.4506 | Iteration Time: 1.0601 sec
[89/200][5/32] 	Loss_D: -0.1395 | Loss_G: -0.3199 | Iteration Time: 0.9886 sec
[89/200][10/32] 	Loss_D: -0.2526 | Loss_G: -0.3976 | Iteration Time: 0.5881 sec
[89/200][15/32] 	Loss_D: -0.2108 | Loss_G: -0.3846 | Iteration Time: 0.4611 sec
[89/200][20/32] 	Loss_D: -0.0463 | Loss_G: -0.2024 | Iteration Time: 0.9831 sec
[89/200][25/32] 	Loss_D: -0.2002 | Loss_G: -0.5520 | Iteration Time: 0.5971 sec
Training Progress:  45%|████▌     | 90/200 [52:17<54:26, 29.69s/it]
[89/200][30/32] 	Loss_D: -0.1901 | Loss_G: -0.3784 | Iteration Time: 0.9501 sec
[90/200][0/32] 	Loss_D: -0.2153 | Loss_G: -0.3485 | Iteration Time: 0.9621 sec
[90/200][5/32] 	Loss_D: -0.2251 | Loss_G: -0.3833 | Iteration Time: 0.9371 sec
[90/200][10/32] 	Loss_D: -0.1608 | Loss_G: -0.3756 | Iteration Time: 0.8846 sec
[90/200][15/32] 	Loss_D: -0.0328 | Loss_G: -0.4326 | Iteration Time: 0.9321 sec
[90/200][20/32] 	Loss_D: -0.1897 | Loss_G: -0.5450 | Iteration Time: 0.4480 sec
[90/200][25/32] 	Loss_D: -0.2025 | Loss_G: -0.4616 | Iteration Time: 1.0351 sec
Training Progress:  46%|████▌     | 91/200 [52:47<54:14, 29.86s/it]
[90/200][30/32] 	Loss_D: 0.0287 | Loss_G: -0.2818 | Iteration Time: 0.9046 sec
[91/200][0/32] 	Loss_D: -0.2056 | Loss_G: -0.3949 | Iteration Time: 1.0641 sec
[91/200][5/32] 	Loss_D: -0.1308 | Loss_G: -0.3601 | Iteration Time: 1.1351 sec
[91/200][10/32] 	Loss_D: -0.1305 | Loss_G: -0.6543 | Iteration Time: 0.4275 sec
[91/200][15/32] 	Loss_D: -0.1379 | Loss_G: -0.2342 | Iteration Time: 1.2966 sec
[91/200][20/32] 	Loss_D: -0.2036 | Loss_G: -0.2471 | Iteration Time: 0.8691 sec
[91/200][25/32] 	Loss_D: -0.2028 | Loss_G: -0.4082 | Iteration Time: 1.1311 sec
[91/200][30/32] 	Loss_D: -0.2146 | Loss_G: -0.2630 | Iteration Time: 1.1366 sec
Training Progress:  46%|████▌     | 92/200 [53:17<53:48, 29.90s/it]
[92/200][0/32] 	Loss_D: -0.2245 | Loss_G: -0.3852 | Iteration Time: 1.0196 sec
[92/200][5/32] 	Loss_D: -0.1805 | Loss_G: -0.3571 | Iteration Time: 1.0001 sec
[92/200][10/32] 	Loss_D: -0.1701 | Loss_G: -0.3859 | Iteration Time: 0.5746 sec
[92/200][15/32] 	Loss_D: -0.2131 | Loss_G: -0.2801 | Iteration Time: 0.4612 sec
[92/200][20/32] 	Loss_D: -0.2073 | Loss_G: -0.5649 | Iteration Time: 1.0141 sec
[92/200][25/32] 	Loss_D: -0.2094 | Loss_G: -0.3663 | Iteration Time: 0.6126 sec
Training Progress:  46%|████▋     | 93/200 [53:45<52:13, 29.29s/it]
[92/200][30/32] 	Loss_D: -0.2183 | Loss_G: -0.3963 | Iteration Time: 0.9066 sec
[93/200][0/32] 	Loss_D: -0.2361 | Loss_G: -0.3743 | Iteration Time: 0.9496 sec
[93/200][5/32] 	Loss_D: -0.2272 | Loss_G: -0.3026 | Iteration Time: 0.9176 sec
[93/200][10/32] 	Loss_D: -0.2115 | Loss_G: -0.3656 | Iteration Time: 0.9031 sec
[93/200][15/32] 	Loss_D: -0.1885 | Loss_G: -0.2437 | Iteration Time: 0.8836 sec
[93/200][20/32] 	Loss_D: -0.1716 | Loss_G: -0.4147 | Iteration Time: 0.4430 sec
Current scores at iteration 3000 | FID: 85.62892150878906 | IS: 2.295743465423584
No description has been provided for this image
[93/200][25/32] 	Loss_D: -0.2078 | Loss_G: -0.3967 | Iteration Time: 1.6107 sec
Training Progress:  47%|████▋     | 94/200 [54:22<55:31, 31.43s/it]
[93/200][30/32] 	Loss_D: -0.2194 | Loss_G: -0.4584 | Iteration Time: 1.2197 sec
[94/200][0/32] 	Loss_D: -0.2598 | Loss_G: -0.3588 | Iteration Time: 1.0346 sec
[94/200][5/32] 	Loss_D: -0.2328 | Loss_G: -0.3685 | Iteration Time: 0.5796 sec
[94/200][10/32] 	Loss_D: -0.1936 | Loss_G: -0.2774 | Iteration Time: 0.8511 sec
[94/200][15/32] 	Loss_D: -0.2266 | Loss_G: -0.4394 | Iteration Time: 0.6346 sec
[94/200][20/32] 	Loss_D: -0.1908 | Loss_G: -0.3753 | Iteration Time: 0.9021 sec
[94/200][25/32] 	Loss_D: -0.2113 | Loss_G: -0.3082 | Iteration Time: 0.7196 sec
Training Progress:  48%|████▊     | 95/200 [54:49<52:52, 30.21s/it]
[94/200][30/32] 	Loss_D: -0.2010 | Loss_G: -0.3383 | Iteration Time: 1.1511 sec
[95/200][0/32] 	Loss_D: -0.1958 | Loss_G: -0.4010 | Iteration Time: 1.0211 sec
[95/200][5/32] 	Loss_D: -0.2037 | Loss_G: -0.2884 | Iteration Time: 0.5941 sec
[95/200][10/32] 	Loss_D: -0.2684 | Loss_G: -0.3783 | Iteration Time: 0.8541 sec
[95/200][15/32] 	Loss_D: -0.2177 | Loss_G: -0.2458 | Iteration Time: 0.6831 sec
[95/200][20/32] 	Loss_D: -0.2000 | Loss_G: -0.4422 | Iteration Time: 0.8961 sec
[95/200][25/32] 	Loss_D: -0.2056 | Loss_G: -0.3496 | Iteration Time: 0.7341 sec
Training Progress:  48%|████▊     | 96/200 [55:16<50:55, 29.38s/it]
[95/200][30/32] 	Loss_D: -0.2059 | Loss_G: -0.3147 | Iteration Time: 1.1506 sec
[96/200][0/32] 	Loss_D: -0.2172 | Loss_G: -0.4236 | Iteration Time: 1.0101 sec
[96/200][5/32] 	Loss_D: -0.1986 | Loss_G: -0.4483 | Iteration Time: 0.5746 sec
[96/200][10/32] 	Loss_D: -0.2331 | Loss_G: -0.3561 | Iteration Time: 0.8551 sec
[96/200][15/32] 	Loss_D: -0.2031 | Loss_G: -0.4115 | Iteration Time: 0.6216 sec
[96/200][20/32] 	Loss_D: -0.2227 | Loss_G: -0.3267 | Iteration Time: 0.8811 sec
[96/200][25/32] 	Loss_D: -0.2309 | Loss_G: -0.3381 | Iteration Time: 0.7171 sec
Training Progress:  48%|████▊     | 97/200 [55:44<49:23, 28.78s/it]
[96/200][30/32] 	Loss_D: -0.2184 | Loss_G: -0.2390 | Iteration Time: 1.1656 sec
[97/200][0/32] 	Loss_D: -0.2291 | Loss_G: -0.3673 | Iteration Time: 1.0157 sec
[97/200][5/32] 	Loss_D: -0.2077 | Loss_G: -0.2788 | Iteration Time: 0.5706 sec
[97/200][10/32] 	Loss_D: -0.2153 | Loss_G: -0.3887 | Iteration Time: 0.8571 sec
[97/200][15/32] 	Loss_D: -0.2218 | Loss_G: -0.3479 | Iteration Time: 0.6346 sec
[97/200][20/32] 	Loss_D: -0.2339 | Loss_G: -0.4846 | Iteration Time: 0.8876 sec
[97/200][25/32] 	Loss_D: -0.0856 | Loss_G: -0.4228 | Iteration Time: 0.7276 sec
Training Progress:  49%|████▉     | 98/200 [56:11<48:10, 28.34s/it]
[97/200][30/32] 	Loss_D: -0.2242 | Loss_G: -0.3441 | Iteration Time: 1.1516 sec
[98/200][0/32] 	Loss_D: -0.2233 | Loss_G: -0.3429 | Iteration Time: 1.0216 sec
[98/200][5/32] 	Loss_D: -0.2401 | Loss_G: -0.4096 | Iteration Time: 0.5896 sec
[98/200][10/32] 	Loss_D: -0.2322 | Loss_G: -0.3846 | Iteration Time: 0.8641 sec
[98/200][15/32] 	Loss_D: -0.2248 | Loss_G: -0.2895 | Iteration Time: 0.6301 sec
[98/200][20/32] 	Loss_D: -0.2224 | Loss_G: -0.2713 | Iteration Time: 0.9016 sec
[98/200][25/32] 	Loss_D: -0.1692 | Loss_G: -0.3688 | Iteration Time: 0.7271 sec
Training Progress:  50%|████▉     | 99/200 [56:38<47:13, 28.05s/it]
[98/200][30/32] 	Loss_D: -0.2051 | Loss_G: -0.2583 | Iteration Time: 1.1556 sec
[99/200][0/32] 	Loss_D: -0.2466 | Loss_G: -0.3929 | Iteration Time: 1.0161 sec
[99/200][5/32] 	Loss_D: -0.2237 | Loss_G: -0.4387 | Iteration Time: 0.5791 sec
[99/200][10/32] 	Loss_D: -0.1989 | Loss_G: -0.2990 | Iteration Time: 0.8591 sec
[99/200][15/32] 	Loss_D: -0.2259 | Loss_G: -0.3978 | Iteration Time: 0.6351 sec
[99/200][20/32] 	Loss_D: -0.2029 | Loss_G: -0.3223 | Iteration Time: 0.8846 sec
[99/200][25/32] 	Loss_D: -0.2062 | Loss_G: -0.2777 | Iteration Time: 0.7091 sec
Training Progress:  50%|█████     | 100/200 [57:06<46:24, 27.85s/it]
[99/200][30/32] 	Loss_D: -0.2073 | Loss_G: -0.3237 | Iteration Time: 1.1716 sec
[100/200][0/32] 	Loss_D: -0.2217 | Loss_G: -0.4547 | Iteration Time: 1.0126 sec
[100/200][5/32] 	Loss_D: -0.2379 | Loss_G: -0.3447 | Iteration Time: 0.5830 sec
[100/200][10/32] 	Loss_D: -0.2064 | Loss_G: -0.3892 | Iteration Time: 0.8651 sec
[100/200][15/32] 	Loss_D: -0.2306 | Loss_G: -0.3922 | Iteration Time: 0.6321 sec
[100/200][20/32] 	Loss_D: -0.2254 | Loss_G: -0.3678 | Iteration Time: 0.8921 sec
[100/200][25/32] 	Loss_D: -0.1596 | Loss_G: -0.3332 | Iteration Time: 0.7326 sec
Training Progress:  50%|█████     | 101/200 [57:33<45:44, 27.72s/it]
[100/200][30/32] 	Loss_D: -0.2242 | Loss_G: -0.4423 | Iteration Time: 1.1721 sec
[101/200][0/32] 	Loss_D: -0.1986 | Loss_G: -0.3631 | Iteration Time: 1.0071 sec
[101/200][5/32] 	Loss_D: -0.2396 | Loss_G: -0.3693 | Iteration Time: 0.5841 sec
[101/200][10/32] 	Loss_D: -0.2320 | Loss_G: -0.3735 | Iteration Time: 0.8501 sec
[101/200][15/32] 	Loss_D: -0.2205 | Loss_G: -0.4531 | Iteration Time: 0.6337 sec
[101/200][20/32] 	Loss_D: -0.2282 | Loss_G: -0.3929 | Iteration Time: 0.8841 sec
[101/200][25/32] 	Loss_D: -0.1886 | Loss_G: -0.3619 | Iteration Time: 0.7296 sec
Training Progress:  51%|█████     | 102/200 [58:01<45:04, 27.60s/it]
[101/200][30/32] 	Loss_D: -0.1643 | Loss_G: -0.1498 | Iteration Time: 1.1616 sec
[102/200][0/32] 	Loss_D: -0.2187 | Loss_G: -0.3680 | Iteration Time: 1.0191 sec
[102/200][5/32] 	Loss_D: -0.2088 | Loss_G: -0.3070 | Iteration Time: 0.5771 sec
[102/200][10/32] 	Loss_D: -0.1947 | Loss_G: -0.3853 | Iteration Time: 0.8371 sec
[102/200][15/32] 	Loss_D: -0.2419 | Loss_G: -0.3442 | Iteration Time: 0.7141 sec
[102/200][20/32] 	Loss_D: -0.2192 | Loss_G: -0.2890 | Iteration Time: 0.9546 sec
[102/200][25/32] 	Loss_D: -0.2327 | Loss_G: -0.3072 | Iteration Time: 0.7701 sec
Training Progress:  52%|█████▏    | 103/200 [58:30<45:16, 28.00s/it]
[102/200][30/32] 	Loss_D: -0.2021 | Loss_G: -0.4683 | Iteration Time: 1.2671 sec
[103/200][0/32] 	Loss_D: -0.2312 | Loss_G: -0.4098 | Iteration Time: 1.0781 sec
[103/200][5/32] 	Loss_D: -0.2405 | Loss_G: -0.3305 | Iteration Time: 0.6036 sec
[103/200][10/32] 	Loss_D: -0.2241 | Loss_G: -0.4391 | Iteration Time: 0.8911 sec
[103/200][15/32] 	Loss_D: -0.2292 | Loss_G: -0.4868 | Iteration Time: 0.6851 sec
[103/200][20/32] 	Loss_D: -0.2341 | Loss_G: -0.3644 | Iteration Time: 0.9461 sec
[103/200][25/32] 	Loss_D: -0.2345 | Loss_G: -0.5382 | Iteration Time: 0.7666 sec
[103/200][30/32] 	Loss_D: -0.1925 | Loss_G: -0.4433 | Iteration Time: 1.2391 sec
Training Progress:  52%|█████▏    | 104/200 [58:59<45:22, 28.36s/it]
[104/200][0/32] 	Loss_D: -0.2157 | Loss_G: -0.3568 | Iteration Time: 1.1042 sec
[104/200][5/32] 	Loss_D: -0.2330 | Loss_G: -0.3429 | Iteration Time: 0.6291 sec
[104/200][10/32] 	Loss_D: -0.2372 | Loss_G: -0.3441 | Iteration Time: 0.8876 sec
[104/200][15/32] 	Loss_D: -0.2376 | Loss_G: -0.3136 | Iteration Time: 0.6966 sec
[104/200][20/32] 	Loss_D: -0.1518 | Loss_G: -0.3157 | Iteration Time: 0.9382 sec
[104/200][25/32] 	Loss_D: -0.0642 | Loss_G: -0.2158 | Iteration Time: 0.7642 sec
Training Progress:  52%|█████▎    | 105/200 [59:28<45:14, 28.57s/it]
[104/200][30/32] 	Loss_D: -0.2279 | Loss_G: -0.3444 | Iteration Time: 1.2226 sec
[105/200][0/32] 	Loss_D: -0.2205 | Loss_G: -0.3558 | Iteration Time: 1.0921 sec
[105/200][5/32] 	Loss_D: -0.1994 | Loss_G: -0.6738 | Iteration Time: 0.6101 sec
[105/200][10/32] 	Loss_D: -0.1673 | Loss_G: -0.1593 | Iteration Time: 0.9106 sec
[105/200][15/32] 	Loss_D: -0.2160 | Loss_G: -0.4569 | Iteration Time: 0.6911 sec
[105/200][20/32] 	Loss_D: -0.1153 | Loss_G: -0.3446 | Iteration Time: 0.9566 sec
[105/200][25/32] 	Loss_D: -0.2072 | Loss_G: -0.3794 | Iteration Time: 0.7681 sec
Training Progress:  53%|█████▎    | 106/200 [59:57<45:03, 28.76s/it]
[105/200][30/32] 	Loss_D: -0.2350 | Loss_G: -0.3545 | Iteration Time: 1.2456 sec
[106/200][0/32] 	Loss_D: -0.2417 | Loss_G: -0.3772 | Iteration Time: 1.0586 sec
[106/200][5/32] 	Loss_D: -0.1978 | Loss_G: -0.2318 | Iteration Time: 0.6131 sec
[106/200][10/32] 	Loss_D: -0.2310 | Loss_G: -0.3594 | Iteration Time: 0.9181 sec
[106/200][15/32] 	Loss_D: -0.2385 | Loss_G: -0.3427 | Iteration Time: 0.7006 sec
[106/200][20/32] 	Loss_D: -0.1623 | Loss_G: -0.4401 | Iteration Time: 0.9536 sec
[106/200][25/32] 	Loss_D: -0.2261 | Loss_G: -0.2903 | Iteration Time: 0.7696 sec
Training Progress:  54%|█████▎    | 107/200 [1:00:26<44:44, 28.87s/it]
[106/200][30/32] 	Loss_D: -0.2340 | Loss_G: -0.2998 | Iteration Time: 1.2432 sec
[107/200][0/32] 	Loss_D: -0.2218 | Loss_G: -0.3845 | Iteration Time: 1.0751 sec
[107/200][5/32] 	Loss_D: -0.2369 | Loss_G: -0.3609 | Iteration Time: 0.6186 sec
[107/200][10/32] 	Loss_D: -0.2108 | Loss_G: -0.3463 | Iteration Time: 0.9071 sec
[107/200][15/32] 	Loss_D: -0.2078 | Loss_G: -0.3651 | Iteration Time: 0.6771 sec
[107/200][20/32] 	Loss_D: -0.2125 | Loss_G: -0.4383 | Iteration Time: 0.9491 sec
[107/200][25/32] 	Loss_D: -0.2358 | Loss_G: -0.2629 | Iteration Time: 0.7661 sec
Training Progress:  54%|█████▍    | 108/200 [1:00:55<44:26, 28.98s/it]
[107/200][30/32] 	Loss_D: -0.2144 | Loss_G: -0.2379 | Iteration Time: 1.2547 sec
[108/200][0/32] 	Loss_D: -0.1993 | Loss_G: -0.3635 | Iteration Time: 1.0916 sec
[108/200][5/32] 	Loss_D: -0.2418 | Loss_G: -0.3398 | Iteration Time: 0.6151 sec
[108/200][10/32] 	Loss_D: -0.2420 | Loss_G: -0.4008 | Iteration Time: 0.9026 sec
[108/200][15/32] 	Loss_D: -0.2280 | Loss_G: -0.4662 | Iteration Time: 0.6791 sec
[108/200][20/32] 	Loss_D: -0.2332 | Loss_G: -0.3898 | Iteration Time: 0.9611 sec
[108/200][25/32] 	Loss_D: -0.2413 | Loss_G: -0.3248 | Iteration Time: 0.7536 sec
[108/200][30/32] 	Loss_D: -0.2394 | Loss_G: -0.3934 | Iteration Time: 1.2346 sec
Training Progress:  55%|█████▍    | 109/200 [1:01:24<44:00, 29.02s/it]
[109/200][0/32] 	Loss_D: -0.2002 | Loss_G: -0.3177 | Iteration Time: 1.0926 sec
[109/200][5/32] 	Loss_D: -0.2375 | Loss_G: -0.3304 | Iteration Time: 0.6231 sec
[109/200][10/32] 	Loss_D: -0.2413 | Loss_G: -0.3966 | Iteration Time: 0.9111 sec
Current scores at iteration 3500 | FID: 74.44414520263672 | IS: 2.2794289588928223
No description has been provided for this image
[109/200][15/32] 	Loss_D: -0.1855 | Loss_G: -0.2217 | Iteration Time: 1.1467 sec
[109/200][20/32] 	Loss_D: -0.0614 | Loss_G: -0.5847 | Iteration Time: 0.8201 sec
[109/200][25/32] 	Loss_D: -0.2385 | Loss_G: -0.3763 | Iteration Time: 1.0866 sec
[109/200][30/32] 	Loss_D: -0.2342 | Loss_G: -0.3776 | Iteration Time: 1.2251 sec
Training Progress:  55%|█████▌    | 110/200 [1:02:01<47:05, 31.40s/it]
[110/200][0/32] 	Loss_D: -0.2045 | Loss_G: -0.3834 | Iteration Time: 1.0211 sec
[110/200][5/32] 	Loss_D: -0.2341 | Loss_G: -0.4305 | Iteration Time: 1.1901 sec
[110/200][10/32] 	Loss_D: -0.2379 | Loss_G: -0.4176 | Iteration Time: 1.2676 sec
[110/200][15/32] 	Loss_D: -0.2364 | Loss_G: -0.2839 | Iteration Time: 0.8606 sec
[110/200][20/32] 	Loss_D: -0.2523 | Loss_G: -0.3626 | Iteration Time: 0.9516 sec
[110/200][25/32] 	Loss_D: -0.2326 | Loss_G: -0.3262 | Iteration Time: 0.9796 sec
[110/200][30/32] 	Loss_D: -0.2306 | Loss_G: -0.3879 | Iteration Time: 0.7961 sec
Training Progress:  56%|█████▌    | 111/200 [1:02:36<47:48, 32.23s/it]
[111/200][0/32] 	Loss_D: -0.2432 | Loss_G: -0.3606 | Iteration Time: 1.0776 sec
[111/200][5/32] 	Loss_D: -0.2032 | Loss_G: -0.6294 | Iteration Time: 0.7826 sec
[111/200][10/32] 	Loss_D: -0.2416 | Loss_G: -0.4062 | Iteration Time: 1.0606 sec
[111/200][15/32] 	Loss_D: -0.2382 | Loss_G: -0.3983 | Iteration Time: 0.9461 sec
[111/200][20/32] 	Loss_D: -0.2382 | Loss_G: -0.5962 | Iteration Time: 1.1122 sec
[111/200][25/32] 	Loss_D: -0.2270 | Loss_G: -0.2788 | Iteration Time: 1.2992 sec
[111/200][30/32] 	Loss_D: -0.2324 | Loss_G: -0.3446 | Iteration Time: 1.2561 sec
Training Progress:  56%|█████▌    | 112/200 [1:03:09<47:46, 32.57s/it]
[112/200][0/32] 	Loss_D: -0.2405 | Loss_G: -0.3614 | Iteration Time: 0.9466 sec
[112/200][5/32] 	Loss_D: -0.2217 | Loss_G: -0.2865 | Iteration Time: 1.2521 sec
[112/200][10/32] 	Loss_D: -0.2137 | Loss_G: -0.3584 | Iteration Time: 0.7776 sec
[112/200][15/32] 	Loss_D: -0.2685 | Loss_G: -0.3764 | Iteration Time: 1.2021 sec
[112/200][20/32] 	Loss_D: -0.2152 | Loss_G: -0.4149 | Iteration Time: 0.7756 sec
[112/200][25/32] 	Loss_D: -0.2502 | Loss_G: -0.3494 | Iteration Time: 1.3012 sec
[112/200][30/32] 	Loss_D: -0.1938 | Loss_G: -0.3358 | Iteration Time: 1.1071 sec
Training Progress:  56%|█████▋    | 113/200 [1:03:42<47:19, 32.63s/it]
[113/200][0/32] 	Loss_D: -0.2321 | Loss_G: -0.4370 | Iteration Time: 1.1052 sec
[113/200][5/32] 	Loss_D: -0.2490 | Loss_G: -0.3716 | Iteration Time: 0.7691 sec
[113/200][10/32] 	Loss_D: -0.2226 | Loss_G: -0.3499 | Iteration Time: 1.0836 sec
[113/200][15/32] 	Loss_D: -0.2100 | Loss_G: -0.2725 | Iteration Time: 0.9121 sec
[113/200][20/32] 	Loss_D: -0.2354 | Loss_G: -0.3066 | Iteration Time: 1.1246 sec
[113/200][25/32] 	Loss_D: -0.2310 | Loss_G: -0.3591 | Iteration Time: 1.2651 sec
[113/200][30/32] 	Loss_D: -0.2255 | Loss_G: -0.2974 | Iteration Time: 1.2571 sec
Training Progress:  57%|█████▋    | 114/200 [1:04:15<47:02, 32.82s/it]
[114/200][0/32] 	Loss_D: -0.2235 | Loss_G: -0.5789 | Iteration Time: 0.9521 sec
[114/200][5/32] 	Loss_D: -0.2162 | Loss_G: -0.2428 | Iteration Time: 1.2526 sec
[114/200][10/32] 	Loss_D: -0.2320 | Loss_G: -0.3016 | Iteration Time: 0.7671 sec
[114/200][15/32] 	Loss_D: -0.2525 | Loss_G: -0.3833 | Iteration Time: 1.2101 sec
[114/200][20/32] 	Loss_D: -0.2469 | Loss_G: -0.3718 | Iteration Time: 0.7541 sec
[114/200][25/32] 	Loss_D: -0.1928 | Loss_G: -0.3668 | Iteration Time: 1.3217 sec
[114/200][30/32] 	Loss_D: -0.2301 | Loss_G: -0.2696 | Iteration Time: 1.1416 sec
Training Progress:  57%|█████▊    | 115/200 [1:04:48<46:35, 32.88s/it]
[115/200][0/32] 	Loss_D: -0.2101 | Loss_G: -0.3793 | Iteration Time: 1.0652 sec
[115/200][5/32] 	Loss_D: -0.1937 | Loss_G: -0.3104 | Iteration Time: 0.7756 sec
[115/200][10/32] 	Loss_D: -0.2044 | Loss_G: -0.4021 | Iteration Time: 1.0666 sec
[115/200][15/32] 	Loss_D: -0.2194 | Loss_G: -0.3802 | Iteration Time: 0.8981 sec
[115/200][20/32] 	Loss_D: -0.2078 | Loss_G: -0.3792 | Iteration Time: 1.1016 sec
[115/200][25/32] 	Loss_D: -0.2502 | Loss_G: -0.3612 | Iteration Time: 1.2511 sec
[115/200][30/32] 	Loss_D: -0.2017 | Loss_G: -0.3593 | Iteration Time: 1.2587 sec
Training Progress:  58%|█████▊    | 116/200 [1:05:21<46:04, 32.91s/it]
[116/200][0/32] 	Loss_D: -0.1806 | Loss_G: -0.4537 | Iteration Time: 0.9652 sec
[116/200][5/32] 	Loss_D: -0.2546 | Loss_G: -0.3780 | Iteration Time: 1.2751 sec
[116/200][10/32] 	Loss_D: -0.2627 | Loss_G: -0.3212 | Iteration Time: 0.7746 sec
[116/200][15/32] 	Loss_D: -0.2225 | Loss_G: -0.4467 | Iteration Time: 1.2121 sec
[116/200][20/32] 	Loss_D: -0.2633 | Loss_G: -0.3252 | Iteration Time: 0.7626 sec
[116/200][25/32] 	Loss_D: -0.2227 | Loss_G: -0.3542 | Iteration Time: 1.3136 sec
[116/200][30/32] 	Loss_D: -0.2329 | Loss_G: -0.3901 | Iteration Time: 1.1176 sec
Training Progress:  58%|█████▊    | 117/200 [1:05:54<45:32, 32.93s/it]
[117/200][0/32] 	Loss_D: -0.2181 | Loss_G: -0.3529 | Iteration Time: 1.0401 sec
[117/200][5/32] 	Loss_D: -0.2316 | Loss_G: -0.3351 | Iteration Time: 0.7461 sec
[117/200][10/32] 	Loss_D: -0.2444 | Loss_G: -0.3901 | Iteration Time: 1.0836 sec
[117/200][15/32] 	Loss_D: -0.2488 | Loss_G: -0.3377 | Iteration Time: 0.8971 sec
[117/200][20/32] 	Loss_D: -0.2268 | Loss_G: -0.2846 | Iteration Time: 1.1116 sec
[117/200][25/32] 	Loss_D: -0.1854 | Loss_G: -0.3927 | Iteration Time: 1.2561 sec
[117/200][30/32] 	Loss_D: -0.2129 | Loss_G: -0.4161 | Iteration Time: 1.2552 sec
Training Progress:  59%|█████▉    | 118/200 [1:06:27<45:10, 33.05s/it]
[118/200][0/32] 	Loss_D: -0.2234 | Loss_G: -0.1972 | Iteration Time: 0.9526 sec
[118/200][5/32] 	Loss_D: -0.2099 | Loss_G: -0.5818 | Iteration Time: 1.2707 sec
[118/200][10/32] 	Loss_D: -0.2287 | Loss_G: -0.3905 | Iteration Time: 0.7736 sec
[118/200][15/32] 	Loss_D: -0.2475 | Loss_G: -0.2631 | Iteration Time: 1.2052 sec
[118/200][20/32] 	Loss_D: -0.1686 | Loss_G: -0.4935 | Iteration Time: 0.7741 sec
[118/200][25/32] 	Loss_D: -0.2529 | Loss_G: -0.3465 | Iteration Time: 1.3276 sec
[118/200][30/32] 	Loss_D: -0.2265 | Loss_G: -0.4465 | Iteration Time: 1.1261 sec
Training Progress:  60%|█████▉    | 119/200 [1:07:00<44:35, 33.03s/it]
[119/200][0/32] 	Loss_D: -0.2339 | Loss_G: -0.3715 | Iteration Time: 1.0826 sec
[119/200][5/32] 	Loss_D: -0.2387 | Loss_G: -0.3318 | Iteration Time: 0.7946 sec
[119/200][10/32] 	Loss_D: -0.2481 | Loss_G: -0.4062 | Iteration Time: 1.0741 sec
[119/200][15/32] 	Loss_D: -0.2494 | Loss_G: -0.3683 | Iteration Time: 0.9131 sec
[119/200][20/32] 	Loss_D: -0.2396 | Loss_G: -0.4680 | Iteration Time: 1.1231 sec
[119/200][25/32] 	Loss_D: -0.2132 | Loss_G: -0.3014 | Iteration Time: 1.2657 sec
[119/200][30/32] 	Loss_D: -0.2370 | Loss_G: -0.4015 | Iteration Time: 1.2546 sec
Training Progress:  60%|██████    | 120/200 [1:07:33<44:04, 33.06s/it]
[120/200][0/32] 	Loss_D: -0.1963 | Loss_G: -0.5959 | Iteration Time: 0.9606 sec
[120/200][5/32] 	Loss_D: -0.2228 | Loss_G: -0.3359 | Iteration Time: 1.2571 sec
[120/200][10/32] 	Loss_D: -0.2207 | Loss_G: -0.2168 | Iteration Time: 0.8046 sec
[120/200][15/32] 	Loss_D: -0.2460 | Loss_G: -0.3460 | Iteration Time: 1.2016 sec
[120/200][20/32] 	Loss_D: -0.2304 | Loss_G: -0.4728 | Iteration Time: 0.7791 sec
[120/200][25/32] 	Loss_D: -0.2029 | Loss_G: -0.2317 | Iteration Time: 1.3192 sec
[120/200][30/32] 	Loss_D: -0.1811 | Loss_G: -0.3646 | Iteration Time: 1.1146 sec
Training Progress:  60%|██████    | 121/200 [1:08:06<43:26, 33.00s/it]
[121/200][0/32] 	Loss_D: -0.2303 | Loss_G: -0.3633 | Iteration Time: 1.0771 sec
[121/200][5/32] 	Loss_D: -0.2309 | Loss_G: -0.5662 | Iteration Time: 0.7686 sec
[121/200][10/32] 	Loss_D: -0.2374 | Loss_G: -0.3945 | Iteration Time: 1.0536 sec
[121/200][15/32] 	Loss_D: -0.2523 | Loss_G: -0.3973 | Iteration Time: 0.8866 sec
[121/200][20/32] 	Loss_D: -0.2353 | Loss_G: -0.3738 | Iteration Time: 1.1296 sec
[121/200][25/32] 	Loss_D: -0.2511 | Loss_G: -0.4028 | Iteration Time: 1.2666 sec
[121/200][30/32] 	Loss_D: -0.1946 | Loss_G: -0.2999 | Iteration Time: 1.2513 sec
Training Progress:  61%|██████    | 122/200 [1:08:39<42:54, 33.01s/it]
[122/200][0/32] 	Loss_D: -0.2673 | Loss_G: -0.3671 | Iteration Time: 0.9351 sec
[122/200][5/32] 	Loss_D: -0.2686 | Loss_G: -0.3228 | Iteration Time: 1.2771 sec
[122/200][10/32] 	Loss_D: -0.2477 | Loss_G: -0.3912 | Iteration Time: 0.7546 sec
[122/200][15/32] 	Loss_D: -0.2261 | Loss_G: -0.3643 | Iteration Time: 1.2012 sec
[122/200][20/32] 	Loss_D: -0.2005 | Loss_G: -0.2420 | Iteration Time: 0.7551 sec
[122/200][25/32] 	Loss_D: -0.2441 | Loss_G: -0.3544 | Iteration Time: 1.3301 sec
[122/200][30/32] 	Loss_D: -0.2498 | Loss_G: -0.3801 | Iteration Time: 1.1236 sec
Training Progress:  62%|██████▏   | 123/200 [1:09:12<42:12, 32.89s/it]
[123/200][0/32] 	Loss_D: -0.2247 | Loss_G: -0.3543 | Iteration Time: 1.0751 sec
[123/200][5/32] 	Loss_D: -0.2319 | Loss_G: -0.3224 | Iteration Time: 0.7736 sec
[123/200][10/32] 	Loss_D: -0.2458 | Loss_G: -0.3220 | Iteration Time: 1.0826 sec
[123/200][15/32] 	Loss_D: -0.2369 | Loss_G: -0.2495 | Iteration Time: 0.8952 sec
[123/200][20/32] 	Loss_D: -0.2307 | Loss_G: -0.3789 | Iteration Time: 1.1387 sec
[123/200][25/32] 	Loss_D: -0.2055 | Loss_G: -0.4294 | Iteration Time: 1.2662 sec
[123/200][30/32] 	Loss_D: -0.1178 | Loss_G: -0.2975 | Iteration Time: 1.2617 sec
Training Progress:  62%|██████▏   | 124/200 [1:09:45<41:48, 33.00s/it]
[124/200][0/32] 	Loss_D: -0.2472 | Loss_G: -0.2946 | Iteration Time: 0.9261 sec
[124/200][5/32] 	Loss_D: -0.1981 | Loss_G: -0.2726 | Iteration Time: 1.2611 sec
[124/200][10/32] 	Loss_D: -0.2382 | Loss_G: -0.3434 | Iteration Time: 0.7736 sec
[124/200][15/32] 	Loss_D: -0.1929 | Loss_G: -0.4405 | Iteration Time: 1.2106 sec
[124/200][20/32] 	Loss_D: -0.2230 | Loss_G: -0.3578 | Iteration Time: 0.8001 sec
[124/200][25/32] 	Loss_D: -0.1951 | Loss_G: -0.2167 | Iteration Time: 1.3192 sec
[124/200][30/32] 	Loss_D: -0.2524 | Loss_G: -0.2938 | Iteration Time: 1.1261 sec
Training Progress:  62%|██████▎   | 125/200 [1:10:18<41:12, 32.97s/it]
[125/200][0/32] 	Loss_D: -0.2309 | Loss_G: -0.3363 | Iteration Time: 1.0976 sec
Current scores at iteration 4000 | FID: 80.17213439941406 | IS: 2.289193630218506
No description has been provided for this image
[125/200][5/32] 	Loss_D: -0.2334 | Loss_G: -0.2217 | Iteration Time: 1.2126 sec
[125/200][10/32] 	Loss_D: -0.2495 | Loss_G: -0.3592 | Iteration Time: 1.4262 sec
[125/200][15/32] 	Loss_D: -0.2595 | Loss_G: -0.3877 | Iteration Time: 1.1671 sec
[125/200][20/32] 	Loss_D: -0.2419 | Loss_G: -0.3736 | Iteration Time: 1.5497 sec
[125/200][25/32] 	Loss_D: -0.2436 | Loss_G: -0.3750 | Iteration Time: 1.2171 sec
Training Progress:  63%|██████▎   | 126/200 [1:11:08<46:48, 37.95s/it]
[125/200][30/32] 	Loss_D: -0.1341 | Loss_G: -0.2067 | Iteration Time: 1.4741 sec
[126/200][0/32] 	Loss_D: -0.2281 | Loss_G: -0.3701 | Iteration Time: 1.1521 sec
[126/200][5/32] 	Loss_D: -0.2333 | Loss_G: -0.3170 | Iteration Time: 1.4907 sec
[126/200][10/32] 	Loss_D: -0.2606 | Loss_G: -0.2559 | Iteration Time: 1.4387 sec
[126/200][15/32] 	Loss_D: -0.2514 | Loss_G: -0.4038 | Iteration Time: 1.1502 sec
[126/200][20/32] 	Loss_D: -0.2285 | Loss_G: -0.3899 | Iteration Time: 1.4367 sec
[126/200][25/32] 	Loss_D: -0.2540 | Loss_G: -0.3303 | Iteration Time: 1.2256 sec
Training Progress:  64%|██████▎   | 127/200 [1:11:50<47:52, 39.34s/it]
[126/200][30/32] 	Loss_D: -0.2279 | Loss_G: -0.3350 | Iteration Time: 1.3907 sec
[127/200][0/32] 	Loss_D: -0.2435 | Loss_G: -0.3409 | Iteration Time: 1.1506 sec
[127/200][5/32] 	Loss_D: -0.2438 | Loss_G: -0.4623 | Iteration Time: 1.4242 sec
[127/200][10/32] 	Loss_D: -0.2599 | Loss_G: -0.3332 | Iteration Time: 1.3417 sec
[127/200][15/32] 	Loss_D: -0.1913 | Loss_G: -0.3141 | Iteration Time: 1.1331 sec
[127/200][20/32] 	Loss_D: -0.2611 | Loss_G: -0.3085 | Iteration Time: 1.3997 sec
[127/200][25/32] 	Loss_D: -0.2468 | Loss_G: -0.3787 | Iteration Time: 1.0646 sec
Training Progress:  64%|██████▍   | 128/200 [1:12:31<47:40, 39.73s/it]
[127/200][30/32] 	Loss_D: -0.2497 | Loss_G: -0.3459 | Iteration Time: 1.3777 sec
[128/200][0/32] 	Loss_D: -0.1859 | Loss_G: -0.4599 | Iteration Time: 1.1006 sec
[128/200][5/32] 	Loss_D: -0.2824 | Loss_G: -0.3453 | Iteration Time: 1.4394 sec
[128/200][10/32] 	Loss_D: -0.2186 | Loss_G: -0.3033 | Iteration Time: 1.3782 sec
[128/200][15/32] 	Loss_D: -0.2351 | Loss_G: -0.3754 | Iteration Time: 1.1732 sec
[128/200][20/32] 	Loss_D: -0.1708 | Loss_G: -0.4040 | Iteration Time: 1.4307 sec
[128/200][25/32] 	Loss_D: -0.2133 | Loss_G: -0.2326 | Iteration Time: 1.1561 sec
Training Progress:  64%|██████▍   | 129/200 [1:13:12<47:41, 40.30s/it]
[128/200][30/32] 	Loss_D: -0.2588 | Loss_G: -0.3602 | Iteration Time: 1.3732 sec
[129/200][0/32] 	Loss_D: -0.1865 | Loss_G: -0.3979 | Iteration Time: 1.1061 sec
[129/200][5/32] 	Loss_D: -0.2384 | Loss_G: -0.3647 | Iteration Time: 1.4487 sec
[129/200][10/32] 	Loss_D: -0.2604 | Loss_G: -0.3639 | Iteration Time: 1.4136 sec
[129/200][15/32] 	Loss_D: -0.2270 | Loss_G: -0.4836 | Iteration Time: 1.0982 sec
[129/200][20/32] 	Loss_D: -0.2433 | Loss_G: -0.2422 | Iteration Time: 1.4067 sec
[129/200][25/32] 	Loss_D: -0.2088 | Loss_G: -0.2171 | Iteration Time: 1.0821 sec
Training Progress:  65%|██████▌   | 130/200 [1:13:53<47:13, 40.47s/it]
[129/200][30/32] 	Loss_D: -0.1188 | Loss_G: -0.6977 | Iteration Time: 1.3811 sec
[130/200][0/32] 	Loss_D: -0.2464 | Loss_G: -0.3485 | Iteration Time: 1.0711 sec
[130/200][5/32] 	Loss_D: -0.2438 | Loss_G: -0.3864 | Iteration Time: 1.4592 sec
[130/200][10/32] 	Loss_D: -0.2787 | Loss_G: -0.4311 | Iteration Time: 1.3446 sec
[130/200][15/32] 	Loss_D: -0.2383 | Loss_G: -0.3490 | Iteration Time: 1.0791 sec
[130/200][20/32] 	Loss_D: -0.1842 | Loss_G: -0.6480 | Iteration Time: 1.3346 sec
[130/200][25/32] 	Loss_D: -0.2143 | Loss_G: -0.3571 | Iteration Time: 1.1622 sec
Training Progress:  66%|██████▌   | 131/200 [1:14:34<46:30, 40.45s/it]
[130/200][30/32] 	Loss_D: -0.2274 | Loss_G: -0.2844 | Iteration Time: 1.3342 sec
[131/200][0/32] 	Loss_D: -0.2247 | Loss_G: -0.4460 | Iteration Time: 1.1321 sec
[131/200][5/32] 	Loss_D: -0.2505 | Loss_G: -0.4810 | Iteration Time: 1.4101 sec
[131/200][10/32] 	Loss_D: -0.2568 | Loss_G: -0.3706 | Iteration Time: 1.3086 sec
[131/200][15/32] 	Loss_D: -0.2683 | Loss_G: -0.3992 | Iteration Time: 1.0576 sec
[131/200][20/32] 	Loss_D: -0.2415 | Loss_G: -0.4140 | Iteration Time: 1.3727 sec
[131/200][25/32] 	Loss_D: -0.2613 | Loss_G: -0.3685 | Iteration Time: 1.0601 sec
Training Progress:  66%|██████▌   | 132/200 [1:15:13<45:31, 40.17s/it]
[131/200][30/32] 	Loss_D: -0.2592 | Loss_G: -0.3110 | Iteration Time: 1.2911 sec
[132/200][0/32] 	Loss_D: -0.2590 | Loss_G: -0.4036 | Iteration Time: 1.0361 sec
[132/200][5/32] 	Loss_D: -0.2531 | Loss_G: -0.4566 | Iteration Time: 1.5628 sec
[132/200][10/32] 	Loss_D: -0.2686 | Loss_G: -0.3740 | Iteration Time: 1.3667 sec
[132/200][15/32] 	Loss_D: -0.2299 | Loss_G: -0.6123 | Iteration Time: 1.0656 sec
[132/200][20/32] 	Loss_D: -0.2488 | Loss_G: -0.2836 | Iteration Time: 1.3151 sec
[132/200][25/32] 	Loss_D: 0.0516 | Loss_G: -0.2989 | Iteration Time: 1.0537 sec
Training Progress:  66%|██████▋   | 133/200 [1:15:53<44:44, 40.07s/it]
[132/200][30/32] 	Loss_D: -0.2409 | Loss_G: -0.4026 | Iteration Time: 1.3131 sec
[133/200][0/32] 	Loss_D: -0.2459 | Loss_G: -0.3488 | Iteration Time: 1.0216 sec
[133/200][5/32] 	Loss_D: -0.2682 | Loss_G: -0.3162 | Iteration Time: 1.4602 sec
[133/200][10/32] 	Loss_D: -0.2291 | Loss_G: -0.2265 | Iteration Time: 1.4632 sec
[133/200][15/32] 	Loss_D: -0.2563 | Loss_G: -0.3581 | Iteration Time: 1.0796 sec
[133/200][20/32] 	Loss_D: -0.2346 | Loss_G: -0.3907 | Iteration Time: 1.3957 sec
[133/200][25/32] 	Loss_D: -0.2243 | Loss_G: -0.3685 | Iteration Time: 1.0646 sec
Training Progress:  67%|██████▋   | 134/200 [1:16:35<44:31, 40.48s/it]
[133/200][30/32] 	Loss_D: -0.2466 | Loss_G: -0.3670 | Iteration Time: 1.2986 sec
[134/200][0/32] 	Loss_D: -0.2279 | Loss_G: -0.3728 | Iteration Time: 1.1196 sec
[134/200][5/32] 	Loss_D: -0.2557 | Loss_G: -0.1971 | Iteration Time: 1.4607 sec
[134/200][10/32] 	Loss_D: -0.2390 | Loss_G: -0.4700 | Iteration Time: 1.4097 sec
[134/200][15/32] 	Loss_D: -0.2252 | Loss_G: -0.3440 | Iteration Time: 1.0716 sec
[134/200][20/32] 	Loss_D: -0.2530 | Loss_G: -0.3656 | Iteration Time: 1.3267 sec
[134/200][25/32] 	Loss_D: -0.2290 | Loss_G: -0.2761 | Iteration Time: 1.0621 sec
Training Progress:  68%|██████▊   | 135/200 [1:17:15<43:48, 40.43s/it]
[134/200][30/32] 	Loss_D: -0.2460 | Loss_G: -0.3617 | Iteration Time: 1.2901 sec
[135/200][0/32] 	Loss_D: -0.2603 | Loss_G: -0.4133 | Iteration Time: 1.0231 sec
[135/200][5/32] 	Loss_D: -0.2583 | Loss_G: -0.3988 | Iteration Time: 1.6792 sec
[135/200][10/32] 	Loss_D: -0.2805 | Loss_G: -0.3895 | Iteration Time: 1.2211 sec
[135/200][15/32] 	Loss_D: -0.2333 | Loss_G: -0.3865 | Iteration Time: 1.0741 sec
[135/200][20/32] 	Loss_D: -0.1595 | Loss_G: -0.3239 | Iteration Time: 1.2377 sec
[135/200][25/32] 	Loss_D: -0.2131 | Loss_G: -0.4758 | Iteration Time: 1.0011 sec
Training Progress:  68%|██████▊   | 136/200 [1:17:55<43:09, 40.46s/it]
[135/200][30/32] 	Loss_D: -0.2587 | Loss_G: -0.3901 | Iteration Time: 1.2318 sec
[136/200][0/32] 	Loss_D: -0.2621 | Loss_G: -0.3229 | Iteration Time: 1.0317 sec
[136/200][5/32] 	Loss_D: -0.2624 | Loss_G: -0.3537 | Iteration Time: 1.4187 sec
[136/200][10/32] 	Loss_D: -0.2644 | Loss_G: -0.2892 | Iteration Time: 1.2567 sec
[136/200][15/32] 	Loss_D: -0.2642 | Loss_G: -0.4374 | Iteration Time: 1.0042 sec
[136/200][20/32] 	Loss_D: -0.1395 | Loss_G: -0.2072 | Iteration Time: 1.2334 sec
[136/200][25/32] 	Loss_D: -0.2371 | Loss_G: -0.3507 | Iteration Time: 1.0089 sec
Training Progress:  68%|██████▊   | 137/200 [1:18:35<42:03, 40.05s/it]
[136/200][30/32] 	Loss_D: -0.2540 | Loss_G: -0.3206 | Iteration Time: 1.2537 sec
[137/200][0/32] 	Loss_D: -0.2273 | Loss_G: -0.2561 | Iteration Time: 0.9908 sec
[137/200][5/32] 	Loss_D: -0.2579 | Loss_G: -0.3136 | Iteration Time: 1.3424 sec
[137/200][10/32] 	Loss_D: -0.2077 | Loss_G: -0.3085 | Iteration Time: 1.2349 sec
[137/200][15/32] 	Loss_D: -0.2543 | Loss_G: -0.3717 | Iteration Time: 1.0188 sec
[137/200][20/32] 	Loss_D: -0.2519 | Loss_G: -0.3589 | Iteration Time: 1.2469 sec
[137/200][25/32] 	Loss_D: -0.1694 | Loss_G: -0.1965 | Iteration Time: 1.0160 sec
Training Progress:  69%|██████▉   | 138/200 [1:19:13<40:58, 39.66s/it]
[137/200][30/32] 	Loss_D: -0.2283 | Loss_G: -0.4726 | Iteration Time: 1.3945 sec
[138/200][0/32] 	Loss_D: -0.2604 | Loss_G: -0.3203 | Iteration Time: 1.1323 sec
[138/200][5/32] 	Loss_D: -0.2786 | Loss_G: -0.3606 | Iteration Time: 1.3172 sec
[138/200][10/32] 	Loss_D: -0.2772 | Loss_G: -0.3883 | Iteration Time: 1.2405 sec
[138/200][15/32] 	Loss_D: -0.2563 | Loss_G: -0.3985 | Iteration Time: 1.0082 sec
[138/200][20/32] 	Loss_D: -0.2630 | Loss_G: -0.3443 | Iteration Time: 1.2543 sec
[138/200][25/32] 	Loss_D: -0.2568 | Loss_G: -0.3795 | Iteration Time: 1.0309 sec
Training Progress:  70%|██████▉   | 139/200 [1:19:52<40:06, 39.45s/it]
[138/200][30/32] 	Loss_D: -0.2694 | Loss_G: -0.3333 | Iteration Time: 1.2425 sec
[139/200][0/32] 	Loss_D: -0.2750 | Loss_G: -0.3655 | Iteration Time: 1.0003 sec
[139/200][5/32] 	Loss_D: -0.2463 | Loss_G: -0.3044 | Iteration Time: 1.3628 sec
[139/200][10/32] 	Loss_D: -0.2614 | Loss_G: -0.3204 | Iteration Time: 1.4403 sec
[139/200][15/32] 	Loss_D: -0.2457 | Loss_G: -0.3616 | Iteration Time: 1.0156 sec
[139/200][20/32] 	Loss_D: -0.1043 | Loss_G: -0.6791 | Iteration Time: 1.3408 sec
[139/200][25/32] 	Loss_D: -0.2728 | Loss_G: -0.4252 | Iteration Time: 1.0672 sec
Training Progress:  70%|███████   | 140/200 [1:20:32<39:28, 39.47s/it]
[139/200][30/32] 	Loss_D: -0.2264 | Loss_G: -0.4066 | Iteration Time: 1.2668 sec
[140/200][0/32] 	Loss_D: -0.2457 | Loss_G: -0.3808 | Iteration Time: 1.0272 sec
[140/200][5/32] 	Loss_D: -0.2238 | Loss_G: -0.3123 | Iteration Time: 1.3442 sec
[140/200][10/32] 	Loss_D: -0.2554 | Loss_G: -0.3820 | Iteration Time: 1.2552 sec
[140/200][15/32] 	Loss_D: -0.2609 | Loss_G: -0.3109 | Iteration Time: 1.0440 sec
[140/200][20/32] 	Loss_D: -0.2449 | Loss_G: -0.5948 | Iteration Time: 1.2706 sec
Current scores at iteration 4500 | FID: 80.69862365722656 | IS: 2.372429847717285
No description has been provided for this image
[140/200][25/32] 	Loss_D: -0.2622 | Loss_G: -0.3551 | Iteration Time: 1.1863 sec
Training Progress:  70%|███████   | 141/200 [1:21:28<43:54, 44.65s/it]
[140/200][30/32] 	Loss_D: -0.2297 | Loss_G: -0.5959 | Iteration Time: 1.4428 sec
[141/200][0/32] 	Loss_D: -0.2696 | Loss_G: -0.3322 | Iteration Time: 1.0832 sec
[141/200][5/32] 	Loss_D: -0.2099 | Loss_G: -0.3207 | Iteration Time: 1.3827 sec
[141/200][10/32] 	Loss_D: -0.2434 | Loss_G: -0.4123 | Iteration Time: 1.5822 sec
[141/200][15/32] 	Loss_D: -0.2162 | Loss_G: -0.4425 | Iteration Time: 1.3342 sec
[141/200][20/32] 	Loss_D: -0.2630 | Loss_G: -0.3742 | Iteration Time: 1.4037 sec
[141/200][25/32] 	Loss_D: -0.2526 | Loss_G: -0.3856 | Iteration Time: 1.1557 sec
Training Progress:  71%|███████   | 142/200 [1:22:11<42:39, 44.14s/it]
[141/200][30/32] 	Loss_D: -0.2635 | Loss_G: -0.4529 | Iteration Time: 1.6402 sec
[142/200][0/32] 	Loss_D: -0.2131 | Loss_G: -0.4464 | Iteration Time: 1.2162 sec
[142/200][5/32] 	Loss_D: -0.2506 | Loss_G: -0.4609 | Iteration Time: 1.1157 sec
[142/200][10/32] 	Loss_D: -0.2351 | Loss_G: -0.3309 | Iteration Time: 1.8208 sec
[142/200][15/32] 	Loss_D: -0.2462 | Loss_G: -0.2191 | Iteration Time: 1.8588 sec
[142/200][20/32] 	Loss_D: -0.2579 | Loss_G: -0.2624 | Iteration Time: 1.0047 sec
[142/200][25/32] 	Loss_D: -0.2633 | Loss_G: -0.4149 | Iteration Time: 1.2177 sec
Training Progress:  72%|███████▏  | 143/200 [1:22:56<42:10, 44.39s/it]
[142/200][30/32] 	Loss_D: -0.2431 | Loss_G: -0.3017 | Iteration Time: 1.6408 sec
[143/200][0/32] 	Loss_D: -0.2496 | Loss_G: -0.3664 | Iteration Time: 1.2452 sec
[143/200][5/32] 	Loss_D: -0.2622 | Loss_G: -0.3656 | Iteration Time: 1.2762 sec
[143/200][10/32] 	Loss_D: -0.2376 | Loss_G: -0.3678 | Iteration Time: 1.2297 sec
[143/200][15/32] 	Loss_D: -0.2679 | Loss_G: -0.3566 | Iteration Time: 1.1532 sec
[143/200][20/32] 	Loss_D: -0.2716 | Loss_G: -0.3868 | Iteration Time: 1.2991 sec
[143/200][25/32] 	Loss_D: -0.2672 | Loss_G: -0.2301 | Iteration Time: 1.8185 sec
Training Progress:  72%|███████▏  | 144/200 [1:23:41<41:32, 44.51s/it]
[143/200][30/32] 	Loss_D: -0.2489 | Loss_G: -0.3813 | Iteration Time: 1.7381 sec
[144/200][0/32] 	Loss_D: -0.2385 | Loss_G: -0.3885 | Iteration Time: 1.3295 sec
[144/200][5/32] 	Loss_D: -0.2582 | Loss_G: -0.2974 | Iteration Time: 1.2229 sec
[144/200][10/32] 	Loss_D: -0.2523 | Loss_G: -0.2551 | Iteration Time: 1.1287 sec
[144/200][15/32] 	Loss_D: -0.2218 | Loss_G: -0.4391 | Iteration Time: 1.6033 sec
[144/200][20/32] 	Loss_D: -0.2463 | Loss_G: -0.4734 | Iteration Time: 1.1083 sec
[144/200][25/32] 	Loss_D: -0.2537 | Loss_G: -0.5012 | Iteration Time: 1.2359 sec
Training Progress:  72%|███████▎  | 145/200 [1:24:27<41:05, 44.83s/it]
[144/200][30/32] 	Loss_D: -0.2408 | Loss_G: -0.3099 | Iteration Time: 1.7730 sec
[145/200][0/32] 	Loss_D: -0.2092 | Loss_G: -0.3849 | Iteration Time: 1.2864 sec
[145/200][5/32] 	Loss_D: -0.2366 | Loss_G: -0.5013 | Iteration Time: 1.4133 sec
[145/200][10/32] 	Loss_D: -0.2535 | Loss_G: -0.2283 | Iteration Time: 1.2569 sec
[145/200][15/32] 	Loss_D: -0.2625 | Loss_G: -0.2754 | Iteration Time: 1.3813 sec
[145/200][20/32] 	Loss_D: -0.2633 | Loss_G: -0.3014 | Iteration Time: 1.3873 sec
[145/200][25/32] 	Loss_D: -0.2523 | Loss_G: -0.3689 | Iteration Time: 1.7758 sec
Training Progress:  73%|███████▎  | 146/200 [1:25:13<40:47, 45.32s/it]
[145/200][30/32] 	Loss_D: -0.2503 | Loss_G: -0.3228 | Iteration Time: 1.8694 sec
[146/200][0/32] 	Loss_D: -0.2429 | Loss_G: -0.3497 | Iteration Time: 1.3413 sec
[146/200][5/32] 	Loss_D: -0.2434 | Loss_G: -0.3627 | Iteration Time: 1.2117 sec
[146/200][10/32] 	Loss_D: -0.2557 | Loss_G: -0.4114 | Iteration Time: 1.1972 sec
[146/200][15/32] 	Loss_D: -0.2674 | Loss_G: -0.4369 | Iteration Time: 1.5127 sec
[146/200][20/32] 	Loss_D: -0.2013 | Loss_G: -0.5358 | Iteration Time: 1.0902 sec
[146/200][25/32] 	Loss_D: -0.1989 | Loss_G: -0.5985 | Iteration Time: 1.2287 sec
Training Progress:  74%|███████▎  | 147/200 [1:25:59<40:04, 45.37s/it]
[146/200][30/32] 	Loss_D: -0.2425 | Loss_G: -0.3825 | Iteration Time: 1.7828 sec
[147/200][0/32] 	Loss_D: -0.2560 | Loss_G: -0.3531 | Iteration Time: 1.3687 sec
[147/200][5/32] 	Loss_D: -0.2634 | Loss_G: -0.3457 | Iteration Time: 1.2702 sec
[147/200][10/32] 	Loss_D: -0.2600 | Loss_G: -0.3022 | Iteration Time: 1.1883 sec
[147/200][15/32] 	Loss_D: -0.2609 | Loss_G: -0.3781 | Iteration Time: 1.1663 sec
[147/200][20/32] 	Loss_D: -0.2368 | Loss_G: -0.4125 | Iteration Time: 1.4273 sec
[147/200][25/32] 	Loss_D: -0.2521 | Loss_G: -0.4372 | Iteration Time: 1.6638 sec
Training Progress:  74%|███████▍  | 148/200 [1:26:43<38:56, 44.93s/it]
[147/200][30/32] 	Loss_D: -0.2597 | Loss_G: -0.2918 | Iteration Time: 1.7378 sec
[148/200][0/32] 	Loss_D: -0.2260 | Loss_G: -0.2818 | Iteration Time: 1.2797 sec
[148/200][5/32] 	Loss_D: -0.2395 | Loss_G: -0.3679 | Iteration Time: 1.2127 sec
[148/200][10/32] 	Loss_D: -0.2485 | Loss_G: -0.3777 | Iteration Time: 1.0882 sec
[148/200][15/32] 	Loss_D: -0.2791 | Loss_G: -0.2969 | Iteration Time: 1.5633 sec
[148/200][20/32] 	Loss_D: -0.2537 | Loss_G: -0.2152 | Iteration Time: 1.0887 sec
[148/200][25/32] 	Loss_D: -0.1942 | Loss_G: -0.1787 | Iteration Time: 1.2392 sec
Training Progress:  74%|███████▍  | 149/200 [1:27:27<37:56, 44.64s/it]
[148/200][30/32] 	Loss_D: -0.2692 | Loss_G: -0.2935 | Iteration Time: 1.6849 sec
[149/200][0/32] 	Loss_D: -0.2609 | Loss_G: -0.4512 | Iteration Time: 1.2625 sec
[149/200][5/32] 	Loss_D: -0.2759 | Loss_G: -0.3592 | Iteration Time: 1.2627 sec
[149/200][10/32] 	Loss_D: -0.2762 | Loss_G: -0.3359 | Iteration Time: 1.1758 sec
[149/200][15/32] 	Loss_D: -0.2687 | Loss_G: -0.4044 | Iteration Time: 1.2047 sec
[149/200][20/32] 	Loss_D: -0.2789 | Loss_G: -0.2917 | Iteration Time: 1.2997 sec
[149/200][25/32] 	Loss_D: -0.2526 | Loss_G: -0.3774 | Iteration Time: 1.6988 sec
Training Progress:  75%|███████▌  | 150/200 [1:28:10<36:53, 44.26s/it]
[149/200][30/32] 	Loss_D: -0.1776 | Loss_G: -0.2351 | Iteration Time: 1.7648 sec
[150/200][0/32] 	Loss_D: -0.2422 | Loss_G: -0.3929 | Iteration Time: 1.2707 sec
[150/200][5/32] 	Loss_D: -0.2948 | Loss_G: -0.2493 | Iteration Time: 1.4338 sec
[150/200][10/32] 	Loss_D: -0.2676 | Loss_G: -0.4775 | Iteration Time: 1.0782 sec
[150/200][15/32] 	Loss_D: -0.2624 | Loss_G: -0.3940 | Iteration Time: 1.4922 sec
[150/200][20/32] 	Loss_D: -0.1881 | Loss_G: -0.6947 | Iteration Time: 1.0947 sec
[150/200][25/32] 	Loss_D: -0.1543 | Loss_G: -0.2986 | Iteration Time: 1.2297 sec
Training Progress:  76%|███████▌  | 151/200 [1:28:54<35:58, 44.05s/it]
[150/200][30/32] 	Loss_D: -0.2583 | Loss_G: -0.2775 | Iteration Time: 1.6904 sec
[151/200][0/32] 	Loss_D: -0.2386 | Loss_G: -0.3820 | Iteration Time: 1.3172 sec
[151/200][5/32] 	Loss_D: -0.1965 | Loss_G: -0.3049 | Iteration Time: 1.2858 sec
[151/200][10/32] 	Loss_D: -0.2665 | Loss_G: -0.3740 | Iteration Time: 1.1632 sec
[151/200][15/32] 	Loss_D: -0.2684 | Loss_G: -0.3060 | Iteration Time: 1.1472 sec
[151/200][20/32] 	Loss_D: -0.2400 | Loss_G: -0.3761 | Iteration Time: 1.2712 sec
[151/200][25/32] 	Loss_D: -0.2827 | Loss_G: -0.3078 | Iteration Time: 1.6758 sec
Training Progress:  76%|███████▌  | 152/200 [1:29:36<34:52, 43.60s/it]
[151/200][30/32] 	Loss_D: -0.2634 | Loss_G: -0.2602 | Iteration Time: 1.7302 sec
[152/200][0/32] 	Loss_D: -0.2470 | Loss_G: -0.3361 | Iteration Time: 1.2777 sec
[152/200][5/32] 	Loss_D: -0.2666 | Loss_G: -0.3320 | Iteration Time: 1.1862 sec
[152/200][10/32] 	Loss_D: -0.2493 | Loss_G: -0.4721 | Iteration Time: 1.2532 sec
[152/200][15/32] 	Loss_D: -0.2512 | Loss_G: -0.2245 | Iteration Time: 1.5708 sec
[152/200][20/32] 	Loss_D: -0.2202 | Loss_G: -0.5279 | Iteration Time: 1.2233 sec
[152/200][25/32] 	Loss_D: -0.1897 | Loss_G: -0.3379 | Iteration Time: 1.2733 sec
Training Progress:  76%|███████▋  | 153/200 [1:30:22<34:39, 44.24s/it]
[152/200][30/32] 	Loss_D: -0.2557 | Loss_G: -0.3555 | Iteration Time: 1.6884 sec
[153/200][0/32] 	Loss_D: -0.2831 | Loss_G: -0.3721 | Iteration Time: 1.2965 sec
[153/200][5/32] 	Loss_D: -0.2338 | Loss_G: -0.4380 | Iteration Time: 1.5374 sec
[153/200][10/32] 	Loss_D: -0.2792 | Loss_G: -0.3915 | Iteration Time: 1.1917 sec
[153/200][15/32] 	Loss_D: -0.2340 | Loss_G: -0.3302 | Iteration Time: 1.1737 sec
[153/200][20/32] 	Loss_D: -0.2332 | Loss_G: -0.3489 | Iteration Time: 1.4065 sec
[153/200][25/32] 	Loss_D: -0.2725 | Loss_G: -0.3335 | Iteration Time: 1.7310 sec
Training Progress:  77%|███████▋  | 154/200 [1:31:07<34:01, 44.38s/it]
[153/200][30/32] 	Loss_D: -0.2355 | Loss_G: -0.3113 | Iteration Time: 1.7482 sec
[154/200][0/32] 	Loss_D: -0.2558 | Loss_G: -0.3503 | Iteration Time: 1.2984 sec
[154/200][5/32] 	Loss_D: -0.2766 | Loss_G: -0.2796 | Iteration Time: 1.2618 sec
[154/200][10/32] 	Loss_D: -0.2503 | Loss_G: -0.1853 | Iteration Time: 1.1103 sec
[154/200][15/32] 	Loss_D: -0.2580 | Loss_G: -0.2865 | Iteration Time: 1.5563 sec
[154/200][20/32] 	Loss_D: -0.2484 | Loss_G: -0.4692 | Iteration Time: 1.0972 sec
[154/200][25/32] 	Loss_D: -0.2919 | Loss_G: -0.3172 | Iteration Time: 1.2293 sec
Training Progress:  78%|███████▊  | 155/200 [1:31:51<33:23, 44.53s/it]
[154/200][30/32] 	Loss_D: -0.2697 | Loss_G: -0.3347 | Iteration Time: 1.6712 sec
[155/200][0/32] 	Loss_D: -0.2413 | Loss_G: -0.2585 | Iteration Time: 1.2687 sec
[155/200][5/32] 	Loss_D: -0.2299 | Loss_G: -0.6141 | Iteration Time: 1.2633 sec
[155/200][10/32] 	Loss_D: -0.2582 | Loss_G: -0.2645 | Iteration Time: 1.1673 sec
[155/200][15/32] 	Loss_D: -0.2572 | Loss_G: -0.3061 | Iteration Time: 1.1368 sec
[155/200][20/32] 	Loss_D: -0.2675 | Loss_G: -0.3613 | Iteration Time: 1.2851 sec
[155/200][25/32] 	Loss_D: -0.2625 | Loss_G: -0.3140 | Iteration Time: 1.6958 sec
Training Progress:  78%|███████▊  | 156/200 [1:32:35<32:21, 44.11s/it]
[155/200][30/32] 	Loss_D: -0.2789 | Loss_G: -0.3844 | Iteration Time: 1.7134 sec
[156/200][0/32] 	Loss_D: -0.2302 | Loss_G: -0.3864 | Iteration Time: 1.3383 sec
[156/200][5/32] 	Loss_D: -0.2455 | Loss_G: -0.3886 | Iteration Time: 1.1977 sec
Current scores at iteration 5000 | FID: 80.43878936767578 | IS: 2.3544535636901855
No description has been provided for this image
[156/200][10/32] 	Loss_D: -0.2952 | Loss_G: -0.4769 | Iteration Time: 1.3828 sec
[156/200][15/32] 	Loss_D: -0.1316 | Loss_G: -0.1840 | Iteration Time: 1.0904 sec
[156/200][20/32] 	Loss_D: -0.2845 | Loss_G: -0.3242 | Iteration Time: 1.6761 sec
[156/200][25/32] 	Loss_D: -0.2465 | Loss_G: -0.3056 | Iteration Time: 0.9812 sec
[156/200][30/32] 	Loss_D: -0.2788 | Loss_G: -0.3257 | Iteration Time: 1.8059 sec
Training Progress:  78%|███████▊  | 157/200 [1:33:33<34:43, 48.45s/it]
[157/200][0/32] 	Loss_D: -0.2477 | Loss_G: -0.3393 | Iteration Time: 1.5529 sec
[157/200][5/32] 	Loss_D: -0.2686 | Loss_G: -0.3049 | Iteration Time: 1.6438 sec
[157/200][10/32] 	Loss_D: -0.2311 | Loss_G: -0.3934 | Iteration Time: 0.9684 sec
[157/200][15/32] 	Loss_D: -0.2584 | Loss_G: -0.3146 | Iteration Time: 1.7105 sec
[157/200][20/32] 	Loss_D: -0.2753 | Loss_G: -0.3313 | Iteration Time: 1.0633 sec
[157/200][25/32] 	Loss_D: -0.2758 | Loss_G: -0.3609 | Iteration Time: 1.6900 sec
[157/200][30/32] 	Loss_D: -0.2847 | Loss_G: -0.3234 | Iteration Time: 0.9943 sec
Training Progress:  79%|███████▉  | 158/200 [1:34:17<33:02, 47.21s/it]
[158/200][0/32] 	Loss_D: -0.2394 | Loss_G: -0.3913 | Iteration Time: 1.5042 sec
[158/200][5/32] 	Loss_D: -0.2424 | Loss_G: -0.2270 | Iteration Time: 1.5363 sec
[158/200][10/32] 	Loss_D: -0.2472 | Loss_G: -0.3684 | Iteration Time: 1.0127 sec
[158/200][15/32] 	Loss_D: -0.2817 | Loss_G: -0.3335 | Iteration Time: 1.8699 sec
[158/200][20/32] 	Loss_D: -0.2750 | Loss_G: -0.2921 | Iteration Time: 0.9136 sec
[158/200][25/32] 	Loss_D: -0.1570 | Loss_G: -0.2008 | Iteration Time: 1.8533 sec
[158/200][30/32] 	Loss_D: -0.2627 | Loss_G: -0.3499 | Iteration Time: 1.0357 sec
Training Progress:  80%|███████▉  | 159/200 [1:35:02<31:37, 46.28s/it]
[159/200][0/32] 	Loss_D: -0.2369 | Loss_G: -0.3473 | Iteration Time: 1.5653 sec
[159/200][5/32] 	Loss_D: -0.2853 | Loss_G: -0.3809 | Iteration Time: 1.5648 sec
[159/200][10/32] 	Loss_D: -0.2610 | Loss_G: -0.3638 | Iteration Time: 1.0827 sec
[159/200][15/32] 	Loss_D: -0.2559 | Loss_G: -0.3328 | Iteration Time: 1.6737 sec
[159/200][20/32] 	Loss_D: -0.2748 | Loss_G: -0.3667 | Iteration Time: 0.9061 sec
[159/200][25/32] 	Loss_D: -0.2730 | Loss_G: -0.3191 | Iteration Time: 1.7348 sec
[159/200][30/32] 	Loss_D: -0.2651 | Loss_G: -0.3968 | Iteration Time: 1.1917 sec
Training Progress:  80%|████████  | 160/200 [1:35:45<30:16, 45.41s/it]
[160/200][0/32] 	Loss_D: -0.2578 | Loss_G: -0.3393 | Iteration Time: 1.4912 sec
[160/200][5/32] 	Loss_D: -0.1563 | Loss_G: -0.2978 | Iteration Time: 1.7493 sec
[160/200][10/32] 	Loss_D: -0.2796 | Loss_G: -0.2604 | Iteration Time: 1.2243 sec
[160/200][15/32] 	Loss_D: -0.2692 | Loss_G: -0.3351 | Iteration Time: 1.7243 sec
[160/200][20/32] 	Loss_D: -0.2724 | Loss_G: -0.3063 | Iteration Time: 0.9192 sec
[160/200][25/32] 	Loss_D: -0.2595 | Loss_G: -0.3080 | Iteration Time: 1.9469 sec
[160/200][30/32] 	Loss_D: -0.2489 | Loss_G: -0.4501 | Iteration Time: 1.1652 sec
Training Progress:  80%|████████  | 161/200 [1:36:30<29:32, 45.46s/it]
[161/200][0/32] 	Loss_D: -0.2538 | Loss_G: -0.3672 | Iteration Time: 1.6618 sec
[161/200][5/32] 	Loss_D: -0.2529 | Loss_G: -0.3665 | Iteration Time: 1.5497 sec
[161/200][10/32] 	Loss_D: -0.2236 | Loss_G: -0.2105 | Iteration Time: 1.0542 sec
[161/200][15/32] 	Loss_D: -0.2874 | Loss_G: -0.3152 | Iteration Time: 1.8498 sec
[161/200][20/32] 	Loss_D: -0.2822 | Loss_G: -0.3121 | Iteration Time: 0.9142 sec
[161/200][25/32] 	Loss_D: -0.2491 | Loss_G: -0.3881 | Iteration Time: 1.7563 sec
[161/200][30/32] 	Loss_D: -0.2569 | Loss_G: -0.3191 | Iteration Time: 1.0472 sec
Training Progress:  81%|████████  | 162/200 [1:37:16<28:52, 45.60s/it]
[162/200][0/32] 	Loss_D: -0.2362 | Loss_G: -0.3420 | Iteration Time: 1.4918 sec
[162/200][5/32] 	Loss_D: -0.2663 | Loss_G: -0.3423 | Iteration Time: 1.5307 sec
[162/200][10/32] 	Loss_D: -0.2655 | Loss_G: -0.4147 | Iteration Time: 1.0472 sec
[162/200][15/32] 	Loss_D: -0.2553 | Loss_G: -0.3655 | Iteration Time: 1.7918 sec
[162/200][20/32] 	Loss_D: -0.2856 | Loss_G: -0.3616 | Iteration Time: 0.8982 sec
[162/200][25/32] 	Loss_D: -0.2803 | Loss_G: -0.3056 | Iteration Time: 1.7213 sec
[162/200][30/32] 	Loss_D: -0.2597 | Loss_G: -0.3785 | Iteration Time: 1.2147 sec
Training Progress:  82%|████████▏ | 163/200 [1:38:01<27:52, 45.21s/it]
[163/200][0/32] 	Loss_D: -0.2724 | Loss_G: -0.3306 | Iteration Time: 1.7609 sec
[163/200][5/32] 	Loss_D: -0.2894 | Loss_G: -0.3750 | Iteration Time: 1.5563 sec
[163/200][10/32] 	Loss_D: -0.2977 | Loss_G: -0.4526 | Iteration Time: 1.0897 sec
[163/200][15/32] 	Loss_D: -0.2800 | Loss_G: -0.3314 | Iteration Time: 1.6850 sec
[163/200][20/32] 	Loss_D: -0.2672 | Loss_G: -0.2638 | Iteration Time: 0.9094 sec
[163/200][25/32] 	Loss_D: -0.2386 | Loss_G: -0.2038 | Iteration Time: 1.7556 sec
[163/200][30/32] 	Loss_D: -0.2152 | Loss_G: -0.3804 | Iteration Time: 1.0763 sec
Training Progress:  82%|████████▏ | 164/200 [1:38:45<26:58, 44.96s/it]
[164/200][0/32] 	Loss_D: -0.1894 | Loss_G: -0.4375 | Iteration Time: 1.5039 sec
[164/200][5/32] 	Loss_D: -0.2702 | Loss_G: -0.4299 | Iteration Time: 1.6878 sec
[164/200][10/32] 	Loss_D: -0.2851 | Loss_G: -0.3616 | Iteration Time: 1.0587 sec
[164/200][15/32] 	Loss_D: -0.2809 | Loss_G: -0.4985 | Iteration Time: 1.8940 sec
[164/200][20/32] 	Loss_D: -0.2620 | Loss_G: -0.1506 | Iteration Time: 0.8688 sec
[164/200][25/32] 	Loss_D: -0.2778 | Loss_G: -0.3570 | Iteration Time: 1.7551 sec
[164/200][30/32] 	Loss_D: -0.2590 | Loss_G: -0.3270 | Iteration Time: 1.0797 sec
Training Progress:  82%|████████▎ | 165/200 [1:39:30<26:11, 44.91s/it]
[165/200][0/32] 	Loss_D: -0.2853 | Loss_G: -0.3959 | Iteration Time: 1.5968 sec
[165/200][5/32] 	Loss_D: -0.2902 | Loss_G: -0.2875 | Iteration Time: 1.5788 sec
[165/200][10/32] 	Loss_D: -0.2627 | Loss_G: -0.4233 | Iteration Time: 1.0437 sec
[165/200][15/32] 	Loss_D: -0.2678 | Loss_G: -0.2746 | Iteration Time: 1.6893 sec
[165/200][20/32] 	Loss_D: -0.2868 | Loss_G: -0.2767 | Iteration Time: 0.9422 sec
[165/200][25/32] 	Loss_D: -0.2283 | Loss_G: -0.2405 | Iteration Time: 2.0704 sec
[165/200][30/32] 	Loss_D: -0.2686 | Loss_G: -0.3324 | Iteration Time: 1.0257 sec
Training Progress:  83%|████████▎ | 166/200 [1:40:13<25:12, 44.49s/it]
[166/200][0/32] 	Loss_D: -0.2691 | Loss_G: -0.3817 | Iteration Time: 1.4282 sec
[166/200][5/32] 	Loss_D: -0.2324 | Loss_G: -0.3913 | Iteration Time: 1.5157 sec
[166/200][10/32] 	Loss_D: -0.2239 | Loss_G: -0.5188 | Iteration Time: 1.0312 sec
[166/200][15/32] 	Loss_D: -0.1914 | Loss_G: -0.6482 | Iteration Time: 1.8788 sec
[166/200][20/32] 	Loss_D: -0.2477 | Loss_G: -0.4108 | Iteration Time: 0.8876 sec
[166/200][25/32] 	Loss_D: -0.2862 | Loss_G: -0.2781 | Iteration Time: 1.9304 sec
[166/200][30/32] 	Loss_D: -0.1871 | Loss_G: -0.5546 | Iteration Time: 1.0452 sec
Training Progress:  84%|████████▎ | 167/200 [1:40:56<24:08, 43.89s/it]
[167/200][0/32] 	Loss_D: -0.2726 | Loss_G: -0.3839 | Iteration Time: 1.4343 sec
[167/200][5/32] 	Loss_D: -0.2634 | Loss_G: -0.5835 | Iteration Time: 1.7554 sec
[167/200][10/32] 	Loss_D: -0.3051 | Loss_G: -0.3825 | Iteration Time: 1.1112 sec
[167/200][15/32] 	Loss_D: -0.2936 | Loss_G: -0.3237 | Iteration Time: 1.8483 sec
[167/200][20/32] 	Loss_D: -0.2932 | Loss_G: -0.3684 | Iteration Time: 0.8992 sec
[167/200][25/32] 	Loss_D: -0.2689 | Loss_G: -0.3941 | Iteration Time: 1.7410 sec
[167/200][30/32] 	Loss_D: -0.2525 | Loss_G: -0.3449 | Iteration Time: 1.0378 sec
Training Progress:  84%|████████▍ | 168/200 [1:41:40<23:26, 43.95s/it]
[168/200][0/32] 	Loss_D: -0.2607 | Loss_G: -0.3620 | Iteration Time: 1.5225 sec
[168/200][5/32] 	Loss_D: -0.2520 | Loss_G: -0.3300 | Iteration Time: 1.5424 sec
[168/200][10/32] 	Loss_D: -0.2248 | Loss_G: -0.3591 | Iteration Time: 1.0478 sec
[168/200][15/32] 	Loss_D: -0.2653 | Loss_G: -0.3637 | Iteration Time: 1.6975 sec
[168/200][20/32] 	Loss_D: -0.2215 | Loss_G: -0.5466 | Iteration Time: 1.0298 sec
[168/200][25/32] 	Loss_D: -0.2463 | Loss_G: -0.3959 | Iteration Time: 1.7788 sec
[168/200][30/32] 	Loss_D: -0.2833 | Loss_G: -0.3039 | Iteration Time: 1.0312 sec
Training Progress:  84%|████████▍ | 169/200 [1:42:24<22:43, 43.97s/it]
[169/200][0/32] 	Loss_D: -0.2644 | Loss_G: -0.3385 | Iteration Time: 1.4417 sec
[169/200][5/32] 	Loss_D: -0.2304 | Loss_G: -0.2787 | Iteration Time: 1.7188 sec
[169/200][10/32] 	Loss_D: -0.2771 | Loss_G: -0.3371 | Iteration Time: 1.1957 sec
[169/200][15/32] 	Loss_D: -0.2174 | Loss_G: -0.3673 | Iteration Time: 1.6653 sec
[169/200][20/32] 	Loss_D: -0.2677 | Loss_G: -0.4630 | Iteration Time: 1.0722 sec
[169/200][25/32] 	Loss_D: -0.2877 | Loss_G: -0.2902 | Iteration Time: 1.7208 sec
[169/200][30/32] 	Loss_D: -0.2616 | Loss_G: -0.4471 | Iteration Time: 1.0292 sec
Training Progress:  85%|████████▌ | 170/200 [1:43:08<22:04, 44.14s/it]
[170/200][0/32] 	Loss_D: -0.2443 | Loss_G: -0.3567 | Iteration Time: 1.4272 sec
[170/200][5/32] 	Loss_D: -0.2567 | Loss_G: -0.2469 | Iteration Time: 1.5202 sec
[170/200][10/32] 	Loss_D: -0.2656 | Loss_G: -0.3506 | Iteration Time: 1.1777 sec
[170/200][15/32] 	Loss_D: -0.2686 | Loss_G: -0.3653 | Iteration Time: 1.9523 sec
[170/200][20/32] 	Loss_D: -0.2751 | Loss_G: -0.2702 | Iteration Time: 0.9742 sec
[170/200][25/32] 	Loss_D: -0.2777 | Loss_G: -0.2970 | Iteration Time: 1.7153 sec
[170/200][30/32] 	Loss_D: -0.2329 | Loss_G: -0.2886 | Iteration Time: 1.1957 sec
Training Progress:  86%|████████▌ | 171/200 [1:43:52<21:17, 44.05s/it]
[171/200][0/32] 	Loss_D: -0.2326 | Loss_G: -0.3558 | Iteration Time: 1.4863 sec
[171/200][5/32] 	Loss_D: -0.2597 | Loss_G: -0.2944 | Iteration Time: 1.5383 sec
[171/200][10/32] 	Loss_D: -0.1415 | Loss_G: -0.2494 | Iteration Time: 1.0503 sec
[171/200][15/32] 	Loss_D: -0.2809 | Loss_G: -0.3325 | Iteration Time: 1.6627 sec
[171/200][20/32] 	Loss_D: -0.2757 | Loss_G: -0.3352 | Iteration Time: 0.9187 sec
[171/200][25/32] 	Loss_D: -0.2258 | Loss_G: -0.1999 | Iteration Time: 1.7542 sec
Current scores at iteration 5500 | FID: 92.03517150878906 | IS: 2.432176113128662
No description has been provided for this image
Training Progress:  86%|████████▌ | 172/200 [1:44:40<21:03, 45.14s/it]
[171/200][30/32] 	Loss_D: -0.2366 | Loss_G: -0.2203 | Iteration Time: 0.9416 sec
[172/200][0/32] 	Loss_D: -0.1980 | Loss_G: -0.4199 | Iteration Time: 1.4202 sec
[172/200][5/32] 	Loss_D: -0.2998 | Loss_G: -0.2712 | Iteration Time: 0.8546 sec
[172/200][10/32] 	Loss_D: -0.2774 | Loss_G: -0.3582 | Iteration Time: 1.3137 sec
[172/200][15/32] 	Loss_D: -0.3022 | Loss_G: -0.3292 | Iteration Time: 0.8411 sec
[172/200][20/32] 	Loss_D: -0.2437 | Loss_G: -0.1862 | Iteration Time: 0.8946 sec
[172/200][25/32] 	Loss_D: -0.2534 | Loss_G: -0.5277 | Iteration Time: 0.9147 sec
Training Progress:  86%|████████▋ | 173/200 [1:45:11<18:25, 40.94s/it]
[172/200][30/32] 	Loss_D: -0.2076 | Loss_G: -0.1951 | Iteration Time: 1.1451 sec
[173/200][0/32] 	Loss_D: -0.2789 | Loss_G: -0.3471 | Iteration Time: 1.3827 sec
[173/200][5/32] 	Loss_D: -0.2477 | Loss_G: -0.3551 | Iteration Time: 0.9946 sec
[173/200][10/32] 	Loss_D: -0.2777 | Loss_G: -0.4041 | Iteration Time: 0.8081 sec
[173/200][15/32] 	Loss_D: -0.2802 | Loss_G: -0.3127 | Iteration Time: 0.8686 sec
[173/200][20/32] 	Loss_D: -0.2690 | Loss_G: -0.3734 | Iteration Time: 1.1106 sec
[173/200][25/32] 	Loss_D: -0.2405 | Loss_G: -0.2984 | Iteration Time: 0.9396 sec
Training Progress:  87%|████████▋ | 174/200 [1:45:41<16:14, 37.50s/it]
[173/200][30/32] 	Loss_D: -0.2736 | Loss_G: -0.3411 | Iteration Time: 0.8251 sec
[174/200][0/32] 	Loss_D: -0.2768 | Loss_G: -0.3029 | Iteration Time: 1.3417 sec
[174/200][5/32] 	Loss_D: -0.2736 | Loss_G: -0.2448 | Iteration Time: 0.8146 sec
[174/200][10/32] 	Loss_D: -0.2854 | Loss_G: -0.3259 | Iteration Time: 0.8681 sec
[174/200][15/32] 	Loss_D: -0.3083 | Loss_G: -0.3119 | Iteration Time: 1.1551 sec
[174/200][20/32] 	Loss_D: -0.2028 | Loss_G: -0.5877 | Iteration Time: 0.8471 sec
[174/200][25/32] 	Loss_D: -0.2550 | Loss_G: -0.3866 | Iteration Time: 0.8251 sec
Training Progress:  88%|████████▊ | 175/200 [1:46:11<14:44, 35.39s/it]
[174/200][30/32] 	Loss_D: -0.2704 | Loss_G: -0.3940 | Iteration Time: 0.8896 sec
[175/200][0/32] 	Loss_D: -0.2600 | Loss_G: -0.3390 | Iteration Time: 1.3252 sec
[175/200][5/32] 	Loss_D: -0.2822 | Loss_G: -0.4030 | Iteration Time: 0.8916 sec
[175/200][10/32] 	Loss_D: -0.2710 | Loss_G: -0.3880 | Iteration Time: 0.8921 sec
[175/200][15/32] 	Loss_D: -0.2907 | Loss_G: -0.3183 | Iteration Time: 1.1626 sec
[175/200][20/32] 	Loss_D: -0.2674 | Loss_G: -0.4593 | Iteration Time: 0.8516 sec
[175/200][25/32] 	Loss_D: -0.2922 | Loss_G: -0.4155 | Iteration Time: 0.8601 sec
Training Progress:  88%|████████▊ | 176/200 [1:46:42<13:39, 34.16s/it]
[175/200][30/32] 	Loss_D: -0.2656 | Loss_G: -0.2979 | Iteration Time: 1.0471 sec
[176/200][0/32] 	Loss_D: -0.2682 | Loss_G: -0.2884 | Iteration Time: 1.4912 sec
[176/200][5/32] 	Loss_D: -0.2766 | Loss_G: -0.2673 | Iteration Time: 0.8356 sec
[176/200][10/32] 	Loss_D: -0.2515 | Loss_G: -0.4201 | Iteration Time: 1.0866 sec
[176/200][15/32] 	Loss_D: -0.2918 | Loss_G: -0.4324 | Iteration Time: 1.3027 sec
[176/200][20/32] 	Loss_D: -0.2648 | Loss_G: -0.2438 | Iteration Time: 1.0616 sec
[176/200][25/32] 	Loss_D: -0.2636 | Loss_G: -0.2847 | Iteration Time: 0.9416 sec
Training Progress:  88%|████████▊ | 177/200 [1:47:15<12:56, 33.78s/it]
[176/200][30/32] 	Loss_D: -0.2726 | Loss_G: -0.3086 | Iteration Time: 0.8691 sec
[177/200][0/32] 	Loss_D: -0.2708 | Loss_G: -0.3704 | Iteration Time: 1.3402 sec
[177/200][5/32] 	Loss_D: -0.2075 | Loss_G: -0.2884 | Iteration Time: 0.8111 sec
[177/200][10/32] 	Loss_D: -0.3051 | Loss_G: -0.3390 | Iteration Time: 0.9541 sec
[177/200][15/32] 	Loss_D: -0.2407 | Loss_G: -0.4933 | Iteration Time: 1.3507 sec
[177/200][20/32] 	Loss_D: -0.3053 | Loss_G: -0.2272 | Iteration Time: 1.0651 sec
[177/200][25/32] 	Loss_D: -0.2805 | Loss_G: -0.2274 | Iteration Time: 0.8331 sec
Training Progress:  89%|████████▉ | 178/200 [1:47:47<12:11, 33.26s/it]
[177/200][30/32] 	Loss_D: -0.2716 | Loss_G: -0.3946 | Iteration Time: 0.8851 sec
[178/200][0/32] 	Loss_D: -0.0702 | Loss_G: -0.3027 | Iteration Time: 1.4002 sec
[178/200][5/32] 	Loss_D: -0.2749 | Loss_G: -0.1794 | Iteration Time: 0.9271 sec
[178/200][10/32] 	Loss_D: -0.2697 | Loss_G: -0.3617 | Iteration Time: 1.0081 sec
[178/200][15/32] 	Loss_D: -0.2661 | Loss_G: -0.3333 | Iteration Time: 1.1686 sec
[178/200][20/32] 	Loss_D: -0.2664 | Loss_G: -0.5852 | Iteration Time: 0.9896 sec
[178/200][25/32] 	Loss_D: -0.2545 | Loss_G: -0.4138 | Iteration Time: 1.0131 sec
Training Progress:  90%|████████▉ | 179/200 [1:48:21<11:41, 33.41s/it]
[178/200][30/32] 	Loss_D: -0.2408 | Loss_G: -0.2411 | Iteration Time: 0.9306 sec
[179/200][0/32] 	Loss_D: -0.2829 | Loss_G: -0.2352 | Iteration Time: 1.3891 sec
[179/200][5/32] 	Loss_D: -0.2927 | Loss_G: -0.3268 | Iteration Time: 0.8676 sec
[179/200][10/32] 	Loss_D: -0.2739 | Loss_G: -0.4583 | Iteration Time: 0.9251 sec
[179/200][15/32] 	Loss_D: -0.2954 | Loss_G: -0.2881 | Iteration Time: 1.1866 sec
[179/200][20/32] 	Loss_D: -0.2552 | Loss_G: -0.5153 | Iteration Time: 0.9036 sec
[179/200][25/32] 	Loss_D: -0.2824 | Loss_G: -0.3819 | Iteration Time: 1.0626 sec
Training Progress:  90%|█████████ | 180/200 [1:48:53<11:01, 33.06s/it]
[179/200][30/32] 	Loss_D: -0.2434 | Loss_G: -0.2842 | Iteration Time: 1.0656 sec
[180/200][0/32] 	Loss_D: -0.2704 | Loss_G: -0.3683 | Iteration Time: 1.5817 sec
[180/200][5/32] 	Loss_D: -0.2753 | Loss_G: -0.3401 | Iteration Time: 0.9556 sec
[180/200][10/32] 	Loss_D: -0.2821 | Loss_G: -0.3619 | Iteration Time: 1.0796 sec
[180/200][15/32] 	Loss_D: -0.2389 | Loss_G: -0.3289 | Iteration Time: 1.1486 sec
[180/200][20/32] 	Loss_D: -0.2640 | Loss_G: -0.4058 | Iteration Time: 0.8926 sec
[180/200][25/32] 	Loss_D: -0.2527 | Loss_G: -0.3548 | Iteration Time: 0.8591 sec
Training Progress:  90%|█████████ | 181/200 [1:49:26<10:26, 32.99s/it]
[180/200][30/32] 	Loss_D: -0.2288 | Loss_G: -0.3804 | Iteration Time: 0.8806 sec
[181/200][0/32] 	Loss_D: -0.2959 | Loss_G: -0.3704 | Iteration Time: 1.3003 sec
[181/200][5/32] 	Loss_D: -0.3078 | Loss_G: -0.3204 | Iteration Time: 0.8282 sec
[181/200][10/32] 	Loss_D: -0.2965 | Loss_G: -0.3300 | Iteration Time: 0.8532 sec
[181/200][15/32] 	Loss_D: -0.2750 | Loss_G: -0.3420 | Iteration Time: 1.1022 sec
[181/200][20/32] 	Loss_D: -0.2604 | Loss_G: -0.3515 | Iteration Time: 1.0347 sec
[181/200][25/32] 	Loss_D: -0.2893 | Loss_G: -0.3971 | Iteration Time: 0.8477 sec
[181/200][30/32] 	Loss_D: -0.2669 | Loss_G: -0.5300 | Iteration Time: 1.1268 sec
Training Progress:  91%|█████████ | 182/200 [1:49:59<09:50, 32.83s/it]
[182/200][0/32] 	Loss_D: -0.3062 | Loss_G: -0.3493 | Iteration Time: 1.4623 sec
[182/200][5/32] 	Loss_D: -0.2614 | Loss_G: -0.3779 | Iteration Time: 1.0222 sec
[182/200][10/32] 	Loss_D: -0.0194 | Loss_G: -0.6530 | Iteration Time: 1.0517 sec
[182/200][15/32] 	Loss_D: -0.2754 | Loss_G: -0.3638 | Iteration Time: 1.3422 sec
[182/200][20/32] 	Loss_D: -0.2429 | Loss_G: -0.2768 | Iteration Time: 1.0397 sec
[182/200][25/32] 	Loss_D: -0.2226 | Loss_G: -0.5134 | Iteration Time: 0.9592 sec
Training Progress:  92%|█████████▏| 183/200 [1:50:35<09:36, 33.90s/it]
[182/200][30/32] 	Loss_D: -0.2706 | Loss_G: -0.3889 | Iteration Time: 1.0441 sec
[183/200][0/32] 	Loss_D: -0.2910 | Loss_G: -0.2660 | Iteration Time: 1.6248 sec
[183/200][5/32] 	Loss_D: -0.1008 | Loss_G: -0.6020 | Iteration Time: 0.9972 sec
[183/200][10/32] 	Loss_D: -0.2680 | Loss_G: -0.2240 | Iteration Time: 1.0182 sec
[183/200][15/32] 	Loss_D: -0.1937 | Loss_G: -0.6068 | Iteration Time: 1.1882 sec
[183/200][20/32] 	Loss_D: -0.3016 | Loss_G: -0.4445 | Iteration Time: 1.0552 sec
[183/200][25/32] 	Loss_D: -0.2560 | Loss_G: -0.3334 | Iteration Time: 0.9497 sec
Training Progress:  92%|█████████▏| 184/200 [1:51:10<09:08, 34.28s/it]
[183/200][30/32] 	Loss_D: -0.2739 | Loss_G: -0.2104 | Iteration Time: 1.0197 sec
[184/200][0/32] 	Loss_D: -0.2722 | Loss_G: -0.3396 | Iteration Time: 1.5858 sec
[184/200][5/32] 	Loss_D: -0.2918 | Loss_G: -0.4577 | Iteration Time: 0.9387 sec
[184/200][10/32] 	Loss_D: -0.1527 | Loss_G: -0.6810 | Iteration Time: 1.0282 sec
[184/200][15/32] 	Loss_D: -0.2673 | Loss_G: -0.3012 | Iteration Time: 1.2947 sec
[184/200][20/32] 	Loss_D: -0.2954 | Loss_G: -0.3482 | Iteration Time: 1.0507 sec
[184/200][25/32] 	Loss_D: -0.2594 | Loss_G: -0.3118 | Iteration Time: 0.9051 sec
Training Progress:  92%|█████████▎| 185/200 [1:51:45<08:34, 34.31s/it]
[184/200][30/32] 	Loss_D: -0.2817 | Loss_G: -0.2482 | Iteration Time: 0.9066 sec
[185/200][0/32] 	Loss_D: -0.2762 | Loss_G: -0.3549 | Iteration Time: 1.3822 sec
[185/200][5/32] 	Loss_D: -0.3002 | Loss_G: -0.3437 | Iteration Time: 0.8562 sec
[185/200][10/32] 	Loss_D: -0.2906 | Loss_G: -0.3325 | Iteration Time: 0.8967 sec
[185/200][15/32] 	Loss_D: -0.2615 | Loss_G: -0.3201 | Iteration Time: 1.1502 sec
[185/200][20/32] 	Loss_D: -0.2787 | Loss_G: -0.2003 | Iteration Time: 0.8827 sec
[185/200][25/32] 	Loss_D: -0.2705 | Loss_G: -0.3467 | Iteration Time: 0.8551 sec
Training Progress:  93%|█████████▎| 186/200 [1:52:15<07:44, 33.19s/it]
[185/200][30/32] 	Loss_D: -0.1455 | Loss_G: -0.2630 | Iteration Time: 0.8816 sec
[186/200][0/32] 	Loss_D: -0.2730 | Loss_G: -0.3212 | Iteration Time: 1.3657 sec
[186/200][5/32] 	Loss_D: -0.2831 | Loss_G: -0.3407 | Iteration Time: 0.8471 sec
[186/200][10/32] 	Loss_D: -0.3032 | Loss_G: -0.3571 | Iteration Time: 0.8836 sec
[186/200][15/32] 	Loss_D: -0.2664 | Loss_G: -0.3416 | Iteration Time: 1.1332 sec
[186/200][20/32] 	Loss_D: -0.2936 | Loss_G: -0.3661 | Iteration Time: 0.9056 sec
[186/200][25/32] 	Loss_D: -0.2902 | Loss_G: -0.2914 | Iteration Time: 0.8516 sec
Training Progress:  94%|█████████▎| 187/200 [1:52:46<07:03, 32.57s/it]
[186/200][30/32] 	Loss_D: -0.2826 | Loss_G: -0.2924 | Iteration Time: 1.0232 sec
[187/200][0/32] 	Loss_D: -0.2767 | Loss_G: -0.3043 | Iteration Time: 1.5853 sec
[187/200][5/32] 	Loss_D: -0.2921 | Loss_G: -0.3411 | Iteration Time: 0.9536 sec
[187/200][10/32] 	Loss_D: -0.2694 | Loss_G: -0.3216 | Iteration Time: 1.0257 sec
[187/200][15/32] 	Loss_D: -0.3050 | Loss_G: -0.3373 | Iteration Time: 1.2937 sec
Current scores at iteration 6000 | FID: 82.63716125488281 | IS: 2.360400676727295
No description has been provided for this image
[187/200][20/32] 	Loss_D: -0.2732 | Loss_G: -0.2722 | Iteration Time: 0.6016 sec
[187/200][25/32] 	Loss_D: -0.2812 | Loss_G: -0.2858 | Iteration Time: 1.4188 sec
Training Progress:  94%|█████████▍| 188/200 [1:53:27<06:59, 34.99s/it]
[187/200][30/32] 	Loss_D: -0.3045 | Loss_G: -0.2878 | Iteration Time: 1.2892 sec
[188/200][0/32] 	Loss_D: -0.2345 | Loss_G: -0.3486 | Iteration Time: 0.9492 sec
[188/200][5/32] 	Loss_D: -0.3050 | Loss_G: -0.3768 | Iteration Time: 1.0383 sec
[188/200][10/32] 	Loss_D: -0.2769 | Loss_G: -0.3409 | Iteration Time: 1.1427 sec
[188/200][15/32] 	Loss_D: -0.2990 | Loss_G: -0.2314 | Iteration Time: 1.0912 sec
[188/200][20/32] 	Loss_D: -0.2956 | Loss_G: -0.4641 | Iteration Time: 0.8071 sec
[188/200][25/32] 	Loss_D: -0.3115 | Loss_G: -0.3340 | Iteration Time: 1.0542 sec
Training Progress:  94%|█████████▍| 189/200 [1:53:59<06:14, 34.03s/it]
[188/200][30/32] 	Loss_D: -0.2977 | Loss_G: -0.3407 | Iteration Time: 0.8896 sec
[189/200][0/32] 	Loss_D: -0.2933 | Loss_G: -0.3354 | Iteration Time: 0.8026 sec
[189/200][5/32] 	Loss_D: -0.2409 | Loss_G: -0.5460 | Iteration Time: 1.1572 sec
[189/200][10/32] 	Loss_D: -0.2741 | Loss_G: -0.3450 | Iteration Time: 1.1907 sec
[189/200][15/32] 	Loss_D: -0.2351 | Loss_G: -0.6139 | Iteration Time: 0.9862 sec
[189/200][20/32] 	Loss_D: -0.2890 | Loss_G: -0.3767 | Iteration Time: 0.8802 sec
[189/200][25/32] 	Loss_D: -0.2415 | Loss_G: -0.2095 | Iteration Time: 1.0587 sec
Training Progress:  95%|█████████▌| 190/200 [1:54:31<05:34, 33.44s/it]
[189/200][30/32] 	Loss_D: -0.2693 | Loss_G: -0.3194 | Iteration Time: 0.9177 sec
[190/200][0/32] 	Loss_D: -0.3097 | Loss_G: -0.3245 | Iteration Time: 0.8097 sec
[190/200][5/32] 	Loss_D: -0.1781 | Loss_G: -0.3125 | Iteration Time: 1.2552 sec
[190/200][10/32] 	Loss_D: -0.2815 | Loss_G: -0.2099 | Iteration Time: 1.1307 sec
[190/200][15/32] 	Loss_D: -0.2838 | Loss_G: -0.2868 | Iteration Time: 0.9672 sec
[190/200][20/32] 	Loss_D: -0.2466 | Loss_G: -0.4390 | Iteration Time: 0.8436 sec
[190/200][25/32] 	Loss_D: -0.2829 | Loss_G: -0.3624 | Iteration Time: 1.0962 sec
Training Progress:  96%|█████████▌| 191/200 [1:55:03<04:58, 33.20s/it]
[190/200][30/32] 	Loss_D: -0.2700 | Loss_G: -0.2242 | Iteration Time: 1.0257 sec
[191/200][0/32] 	Loss_D: -0.2738 | Loss_G: -0.3552 | Iteration Time: 0.8672 sec
[191/200][5/32] 	Loss_D: -0.2734 | Loss_G: -0.3648 | Iteration Time: 1.0572 sec
[191/200][10/32] 	Loss_D: -0.3192 | Loss_G: -0.3272 | Iteration Time: 1.2582 sec
[191/200][15/32] 	Loss_D: -0.2732 | Loss_G: -0.3626 | Iteration Time: 0.9776 sec
[191/200][20/32] 	Loss_D: -0.2669 | Loss_G: -0.3933 | Iteration Time: 0.8577 sec
[191/200][25/32] 	Loss_D: -0.2988 | Loss_G: -0.3101 | Iteration Time: 1.0562 sec
Training Progress:  96%|█████████▌| 192/200 [1:55:35<04:22, 32.78s/it]
[191/200][30/32] 	Loss_D: -0.2695 | Loss_G: -0.2766 | Iteration Time: 0.9537 sec
[192/200][0/32] 	Loss_D: -0.2899 | Loss_G: -0.3349 | Iteration Time: 0.8457 sec
[192/200][5/32] 	Loss_D: -0.2879 | Loss_G: -0.4499 | Iteration Time: 1.0532 sec
[192/200][10/32] 	Loss_D: -0.2712 | Loss_G: -0.3666 | Iteration Time: 1.1262 sec
[192/200][15/32] 	Loss_D: -0.2970 | Loss_G: -0.4423 | Iteration Time: 1.0307 sec
[192/200][20/32] 	Loss_D: -0.3211 | Loss_G: -0.3186 | Iteration Time: 0.8632 sec
[192/200][25/32] 	Loss_D: -0.2718 | Loss_G: -0.4757 | Iteration Time: 1.1087 sec
Training Progress:  96%|█████████▋| 193/200 [1:56:07<03:47, 32.54s/it]
[192/200][30/32] 	Loss_D: -0.2712 | Loss_G: -0.2423 | Iteration Time: 0.9251 sec
[193/200][0/32] 	Loss_D: -0.2992 | Loss_G: -0.3579 | Iteration Time: 0.9387 sec
[193/200][5/32] 	Loss_D: -0.2862 | Loss_G: -0.4109 | Iteration Time: 1.1072 sec
[193/200][10/32] 	Loss_D: -0.2532 | Loss_G: -0.2449 | Iteration Time: 1.1447 sec
[193/200][15/32] 	Loss_D: -0.3004 | Loss_G: -0.2118 | Iteration Time: 0.9927 sec
[193/200][20/32] 	Loss_D: -0.1145 | Loss_G: -0.3189 | Iteration Time: 0.8447 sec
[193/200][25/32] 	Loss_D: -0.2787 | Loss_G: -0.2107 | Iteration Time: 1.1322 sec
Training Progress:  97%|█████████▋| 194/200 [1:56:39<03:13, 32.30s/it]
[193/200][30/32] 	Loss_D: -0.2876 | Loss_G: -0.3190 | Iteration Time: 0.9067 sec
[194/200][0/32] 	Loss_D: -0.2964 | Loss_G: -0.3425 | Iteration Time: 0.8082 sec
[194/200][5/32] 	Loss_D: -0.2920 | Loss_G: -0.3495 | Iteration Time: 1.0352 sec
[194/200][10/32] 	Loss_D: -0.2258 | Loss_G: -0.2413 | Iteration Time: 1.1212 sec
[194/200][15/32] 	Loss_D: -0.2837 | Loss_G: -0.3945 | Iteration Time: 0.9962 sec
[194/200][20/32] 	Loss_D: -0.2887 | Loss_G: -0.3047 | Iteration Time: 0.8336 sec
[194/200][25/32] 	Loss_D: -0.2900 | Loss_G: -0.3829 | Iteration Time: 1.1362 sec
Training Progress:  98%|█████████▊| 195/200 [1:57:10<02:39, 31.99s/it]
[194/200][30/32] 	Loss_D: -0.2817 | Loss_G: -0.2044 | Iteration Time: 0.8887 sec
[195/200][0/32] 	Loss_D: -0.2527 | Loss_G: -0.3680 | Iteration Time: 0.8177 sec
[195/200][5/32] 	Loss_D: -0.2761 | Loss_G: -0.3678 | Iteration Time: 1.0377 sec
[195/200][10/32] 	Loss_D: -0.3027 | Loss_G: -0.3723 | Iteration Time: 1.1072 sec
[195/200][15/32] 	Loss_D: -0.2872 | Loss_G: -0.3055 | Iteration Time: 0.9822 sec
[195/200][20/32] 	Loss_D: -0.3197 | Loss_G: -0.3104 | Iteration Time: 0.8591 sec
[195/200][25/32] 	Loss_D: -0.2649 | Loss_G: -0.3898 | Iteration Time: 1.1332 sec
Training Progress:  98%|█████████▊| 196/200 [1:57:42<02:07, 31.92s/it]
[195/200][30/32] 	Loss_D: -0.2691 | Loss_G: -0.3877 | Iteration Time: 0.8947 sec
[196/200][0/32] 	Loss_D: -0.2818 | Loss_G: -0.3438 | Iteration Time: 0.8352 sec
[196/200][5/32] 	Loss_D: -0.2843 | Loss_G: -0.2056 | Iteration Time: 1.0642 sec
[196/200][10/32] 	Loss_D: -0.3241 | Loss_G: -0.4841 | Iteration Time: 1.1367 sec
[196/200][15/32] 	Loss_D: -0.2789 | Loss_G: -0.3484 | Iteration Time: 0.9742 sec
[196/200][20/32] 	Loss_D: -0.2625 | Loss_G: -0.3533 | Iteration Time: 0.9717 sec
[196/200][25/32] 	Loss_D: -0.2792 | Loss_G: -0.3379 | Iteration Time: 1.0742 sec
Training Progress:  98%|█████████▊| 197/200 [1:58:14<01:35, 31.82s/it]
[196/200][30/32] 	Loss_D: -0.2602 | Loss_G: -0.3318 | Iteration Time: 0.9262 sec
[197/200][0/32] 	Loss_D: -0.2810 | Loss_G: -0.3488 | Iteration Time: 0.9897 sec
[197/200][5/32] 	Loss_D: -0.2498 | Loss_G: -0.4733 | Iteration Time: 1.1272 sec
[197/200][10/32] 	Loss_D: -0.2906 | Loss_G: -0.3831 | Iteration Time: 1.1642 sec
[197/200][15/32] 	Loss_D: -0.2061 | Loss_G: -0.2257 | Iteration Time: 0.9847 sec
[197/200][20/32] 	Loss_D: -0.2546 | Loss_G: -0.3266 | Iteration Time: 0.8221 sec
[197/200][25/32] 	Loss_D: -0.2928 | Loss_G: -0.2973 | Iteration Time: 1.0722 sec
Training Progress:  99%|█████████▉| 198/200 [1:58:45<01:03, 31.80s/it]
[197/200][30/32] 	Loss_D: -0.2306 | Loss_G: -0.3055 | Iteration Time: 0.9286 sec
[198/200][0/32] 	Loss_D: -0.2824 | Loss_G: -0.2960 | Iteration Time: 0.8457 sec
[198/200][5/32] 	Loss_D: -0.2997 | Loss_G: -0.3170 | Iteration Time: 1.0507 sec
[198/200][10/32] 	Loss_D: -0.2745 | Loss_G: -0.3119 | Iteration Time: 1.1607 sec
[198/200][15/32] 	Loss_D: -0.2646 | Loss_G: -0.3531 | Iteration Time: 1.0017 sec
[198/200][20/32] 	Loss_D: -0.1958 | Loss_G: -0.2574 | Iteration Time: 0.8176 sec
[198/200][25/32] 	Loss_D: -0.2650 | Loss_G: -0.2720 | Iteration Time: 1.0672 sec
Training Progress: 100%|█████████▉| 199/200 [1:59:16<00:31, 31.62s/it]
[198/200][30/32] 	Loss_D: -0.1730 | Loss_G: -0.4902 | Iteration Time: 0.8922 sec
[199/200][0/32] 	Loss_D: -0.2714 | Loss_G: -0.3228 | Iteration Time: 0.8147 sec
[199/200][5/32] 	Loss_D: -0.2766 | Loss_G: -0.3242 | Iteration Time: 1.0668 sec
[199/200][10/32] 	Loss_D: -0.2983 | Loss_G: -0.3973 | Iteration Time: 1.1232 sec
[199/200][15/32] 	Loss_D: -0.2946 | Loss_G: -0.2840 | Iteration Time: 1.0062 sec
[199/200][20/32] 	Loss_D: -0.2785 | Loss_G: -0.3239 | Iteration Time: 0.8071 sec
[199/200][25/32] 	Loss_D: -0.2821 | Loss_G: -0.3296 | Iteration Time: 1.0602 sec
[199/200][30/32] 	Loss_D: -0.2994 | Loss_G: -0.3301 | Iteration Time: 1.0032 sec
Current scores at iteration 6399 | FID: 140.8679656982422 | IS: 1.508641242980957
No description has been provided for this image
Training Progress: 100%|██████████| 200/200 [1:59:52<00:00, 35.96s/it]

LOSS PLOT¶

In [83]:
wgangp_trainer.plot_loss()
No description has been provided for this image

SCORE PLOT¶

In [84]:
wgangp_trainer.plot_scores()
Minimum FID Score of 74.44414520263672 obtained at iteration of 3500.
Maximum IS Score of 2.5484938621520996.
No description has been provided for this image

EVALUATE BEST WEIGHTS¶

LOAD BEST MODELS¶

WGAN¶
In [85]:
# File directory
directory = 'wgan_weights/'
# Get file names
files = os.listdir(directory)
# Get generator files with their iteration number
generator_files = [(file, int(re.search(r'generator-(\d+)', file).group(1)))
                   for file in files if re.match(r'generator-\d+', file)]
# Sort descending by iteration number as highest number will be best weight
generator_files.sort(key=lambda x: x[1], reverse=True)
# Grab best weights file
if generator_files:
    best_weights = generator_files[0][0]
    print(f"The file with the best weights is: {best_weights}")
else:
    print("No files found.")
# Load best weights
best_wgan = torch.load(directory + best_weights)
summary(best_wgan,verbose=0,device=device)
The file with the best weights is: generator-6000.pth
Out[85]:
=================================================================
Layer (type:depth-idx)                   Param #
=================================================================
├─Sequential: 1-1                        --
|    └─Sequential: 2-1                   --
|    |    └─ConvTranspose2d: 3-1         1,638,400
|    |    └─BatchNorm2d: 3-2             2,048
|    |    └─LeakyReLU: 3-3               --
|    └─Sequential: 2-2                   --
|    |    └─ConvTranspose2d: 3-4         8,388,608
|    |    └─BatchNorm2d: 3-5             1,024
|    |    └─LeakyReLU: 3-6               --
|    └─Sequential: 2-3                   --
|    |    └─ConvTranspose2d: 3-7         2,097,152
|    |    └─BatchNorm2d: 3-8             512
|    |    └─LeakyReLU: 3-9               --
|    └─Sequential: 2-4                   --
|    |    └─ConvTranspose2d: 3-10        524,288
|    |    └─BatchNorm2d: 3-11            256
|    |    └─LeakyReLU: 3-12              --
|    └─ConvTranspose2d: 2-5              6,144
|    └─Tanh: 2-6                         --
=================================================================
Total params: 12,658,432
Trainable params: 12,658,432
Non-trainable params: 0
=================================================================
WGAN-GP¶
In [86]:
# File directory
directory = 'wgan-gp_weights/'
# Get file names
files = os.listdir(directory)
# Get generator files with their iteration number
generator_files = [(file, int(re.search(r'generator-(\d+)', file).group(1)))
                   for file in files if re.match(r'generator-\d+', file)]
# Sort descending by iteration number as highest number will be best weight
generator_files.sort(key=lambda x: x[1], reverse=True)
# Grab best weights file
if generator_files:
    best_weights = generator_files[0][0]
    print(f"The file with the best weights is: {best_weights}")
else:
    print("No files found.")
# Load best weights
best_wgangp = torch.load(directory + best_weights)
summary(best_wgangp,verbose=0,device=device)
The file with the best weights is: generator-3500.pth
Out[86]:
=================================================================
Layer (type:depth-idx)                   Param #
=================================================================
├─Sequential: 1-1                        --
|    └─Sequential: 2-1                   --
|    |    └─ConvTranspose2d: 3-1         819,200
|    |    └─BatchNorm2d: 3-2             1,024
|    |    └─LeakyReLU: 3-3               --
|    └─Sequential: 2-2                   --
|    |    └─ConvTranspose2d: 3-4         2,097,152
|    |    └─BatchNorm2d: 3-5             512
|    |    └─LeakyReLU: 3-6               --
|    └─Sequential: 2-3                   --
|    |    └─ConvTranspose2d: 3-7         524,288
|    |    └─BatchNorm2d: 3-8             256
|    |    └─LeakyReLU: 3-9               --
|    └─Sequential: 2-4                   --
|    |    └─ConvTranspose2d: 3-10        131,072
|    |    └─BatchNorm2d: 3-11            128
|    |    └─LeakyReLU: 3-12              --
|    └─ConvTranspose2d: 2-5              3,072
|    └─Tanh: 2-6                         --
=================================================================
Total params: 3,576,704
Trainable params: 3,576,704
Non-trainable params: 0
=================================================================

EVALUATE QUANTITATIVE METRICS¶

In [87]:
# Evaluation Metric Calculation Function
def calculate_metrics(fake, real, model_name, device='cpu'):
    # Instantiate Metrics Calculation Functions from Torchmetrics
    fid = FrechetInceptionDistance(feature=2048, normalize=True).to(device)
    inception = InceptionScore(normalize=True).to(device)

    # Move the real and fake images to CPU
    real = real.to(device)
    fake = fake.to(device)

    # FID Score calculation
    real_images_norm = GANMonitor.transform(real)
    fid.update(real_images_norm, real=True)
    fake_images_norm = GANMonitor.transform(fake)
    fid.update(fake_images_norm, real=False)
    fid_score = fid.compute().detach()

    # Inception Score calculation
    norm_imgs = GANMonitor.transform(fake.to(device))
    scaled_imgs = (norm_imgs * 255).clamp(0, 255).to(torch.uint8)
    inception.update(scaled_imgs)
    inception_score, _ = inception.compute()
    inception_score = inception_score.detach()

    # Compile the results into a DataFrame
    results_df = pd.DataFrame({
        'FID Score': [fid_score.item()],
        'Inception Score': [inception_score.item()]
    })
    results_df.rename(index={0: model_name}, inplace=True)

    # Return DataFrame
    return results_df

WGAN

In [88]:
# Fixed noise (latent vectors)
fixed_noise = torch.randn(1000, nz, 1, 1, device=device)
# Generate images
generated_images = best_wgan(fixed_noise)
# Calculate metrics compared to test images
calculate_metrics(fake=generated_images, real=images[:1000], model_name='WGAN')
Out[88]:
FID Score Inception Score
WGAN 97.612839 3.647007

WGAN-GP

In [89]:
# Fixed noise (latent vectors)
fixed_noise = torch.randn(1000, nz, 1, 1, device=device)
# Generate images
generated_images = best_wgangp(fixed_noise)
# Calculate metrics compared to test images
calculate_metrics(fake=generated_images, real=images[:1000],model_name='WGAN-GP')
Out[89]:
FID Score Inception Score
WGAN-GP 46.479527 3.593866

EVALUATE IMAGES GENERATED¶

In [90]:
def generate_and_plot_single_image(best_gen, images_per_plot=64, batch_size=8, model_name="model"):
    plt.figure(figsize=(8, 8))  # Adjust figure size if needed
    image_count = 0
    while image_count < images_per_plot:
        fixed_noise = torch.randn(batch_size, nz, 1, 1, device=device)  # Ensure nz and device are defined elsewhere
        batch_images = best_gen(fixed_noise)
        size = batch_images.size(0)
        batch_images = normalize_images(batch_images)
        for i in range(size):
            if image_count >= images_per_plot:
                break
            img = batch_images[i]
            img_np = np.transpose(img.cpu().detach().numpy().clip(0, 1), (1, 2, 0))
            plt.subplot(8, 8, image_count + 1)  # Changed to 8x8 grid
            plt.imshow(img_np)
            plt.axis('off')
            image_count += 1
    plt.tight_layout()
    plt.savefig(f'./images/{model_name}_image_plot.png')
    plt.show()

WGAN

In [91]:
generate_and_plot_single_image(best_wgan, model_name='WGAN')
No description has been provided for this image

WGAN-GP

In [92]:
generate_and_plot_single_image(best_wgangp, model_name='WGAN-GP')
No description has been provided for this image